Hotel Booking Dataset Analysis
Dhruv Raghav
2023-12-04
Data Description
- Rows: 119391
- Columns: 32
INTRODUCTION
Overview of the Dataset
The Hotel Booking Dataset is a comprehensive collection of data related to hotel bookings. It includes various details such as booking status, duration of stay, type of hotel, and customer information.
Source of the Dataset
This dataset was sourced from Kaggle.
Objective of the Analysis
The primary objective of this project is to analyze the hotel booking patterns, identify peak seasons, and understand customer preferences to improve service and maximize revenue.
Features of the Dataset
The dataset contains information about various aspects of hotel bookings. Below are the details of each variable
adr: Average daily rate.adults: Number of adults.agent: ID of the travel agency that made the booking.arrival_date_day_of_month: Day of the month of the arrival date.arrival_date_month: Month of arrival date with 12 categories: “January” to “December”.arrival_date_week_number: Week number of the arrival date.arrival_date_year: Year of arrival date.assigned_room_type: Code for the type of room assigned to the booking. Sometimes differs from the reserved room type.babies: Number of babies.booking_changes: Number of changes/amendments made to the booking.children: Number of children.company: ID of the company/entity responsible for the booking.country: Country of origin in ISO 3155–3:2013 format.customer_type: Type of booking, assuming one of four categories.days_in_waiting_list: Days the booking was in the waiting list.deposit_type: Type of deposit made for the booking.distribution_channel: Booking distribution channel.is_canceled: Indicates if the booking was canceled (1) or not (0).is_repeated_quests: Indicates if the booking is from a repeated guest (1) or not (0).lead_time: Days elapsed between booking and arrival.market_segment: Market segment designation.meal: Type of meal booked.previous_bookings_not_canceled: Number of previous bookings not cancelled.previous_cancellations: Number of previous bookings cancelled.required_car_parking_spaces: Number of required car parking spaces.reservation_status: Last status of the reservation.reservation_status_date: Date of the last reservation status.reserved_room_type: Code of room type reserved.stays_in_weekend_nights: Number of weekend nights stayed.stays_in_week_nights: Number of week nights stayed.total_of_special_requests: Number of special requests made by the customer. ## Potential Applications
The insights derived from this dataset can be pivotal for predictive analysis, customer behavior understanding, and strategic planning in hotel management.
# Load necessary library
library(readr)
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(tidyr)## Warning: package 'tidyr' was built under R version 4.3.2library(caret)## Warning: package 'caret' was built under R version 4.3.2## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.3.2## Loading required package: lattice## Warning: package 'lattice' was built under R version 4.3.2# Read the CSV file into a data frame
hotel_data <- read_csv("G:/semester_1/4_Statistics_R/projects/hotel_management/hotel_bookings.csv/hotel_bookings.csv")## Rows: 119390 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): hotel, arrival_date_month, meal, country, market_segment, distrib...
## dbl (18): is_canceled, lead_time, arrival_date_year, arrival_date_week_numb...
## date (1): reservation_status_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# View the first few rows of the data frame
summary(hotel_data)## 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
## Min. :2014-10-17
## 1st Qu.:2016-02-01
## Median :2016-08-07
## Mean :2016-07-30
## 3rd Qu.:2017-02-08
## Max. :2017-09-14
## head(hotel_data)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <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
## # ℹ 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>,
## # reserved_room_type <chr>, assigned_room_type <chr>, …dim(hotel_data)## [1] 119390 32names(hotel_data)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_week_number"
## [7] "arrival_date_day_of_month" "stays_in_weekend_nights"
## [9] "stays_in_week_nights" "adults"
## [11] "children" "babies"
## [13] "meal" "country"
## [15] "market_segment" "distribution_channel"
## [17] "is_repeated_guest" "previous_cancellations"
## [19] "previous_bookings_not_canceled" "reserved_room_type"
## [21] "assigned_room_type" "booking_changes"
## [23] "deposit_type" "agent"
## [25] "company" "days_in_waiting_list"
## [27] "customer_type" "adr"
## [29] "required_car_parking_spaces" "total_of_special_requests"
## [31] "reservation_status" "reservation_status_date"Data Cleaning
# Replacing missing values with median for numeric columns
hotel_data <- hotel_data %>%
mutate(across(where(is.numeric), ~ ifelse(is.na(.), median(., na.rm = TRUE), .)))
# Replacing missing values with the most common value for categorical columns
hotel_data <- hotel_data %>%
mutate(across(where(is.factor), ~ ifelse(is.na(.), Mode(.), .)))Dropping columns
- I am going to drop company feature, because most of the values in it are null and the arrival_date_week_number because I will not use it.
hotel_data <- hotel_data[, !(names(hotel_data) %in% c('company', 'arrival_date_week_number'))]Handle missing values
- There are few columns that have missing values (children, country, agent, company).
colSums(is.na(hotel_data))## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_day_of_month
## 0 0
## stays_in_weekend_nights stays_in_week_nights
## 0 0
## adults children
## 0 0
## babies meal
## 0 0
## country market_segment
## 0 0
## distribution_channel is_repeated_guest
## 0 0
## previous_cancellations previous_bookings_not_canceled
## 0 0
## reserved_room_type assigned_room_type
## 0 0
## booking_changes deposit_type
## 0 0
## agent days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0summary(hotel_data)## 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_day_of_month stays_in_weekend_nights
## Length:119390 Min. : 1.0 Min. : 0.0000
## Class :character 1st Qu.: 8.0 1st Qu.: 0.0000
## Mode :character Median :16.0 Median : 1.0000
## Mean :15.8 Mean : 0.9276
## 3rd Qu.:23.0 3rd Qu.: 2.0000
## Max. :31.0 Max. :19.0000
## stays_in_week_nights adults children babies
## Min. : 0.0 Min. : 0.000 Min. : 0.0000 Min. : 0.000000
## 1st Qu.: 1.0 1st Qu.: 2.000 1st Qu.: 0.0000 1st Qu.: 0.000000
## Median : 2.0 Median : 2.000 Median : 0.0000 Median : 0.000000
## Mean : 2.5 Mean : 1.856 Mean : 0.1039 Mean : 0.007949
## 3rd Qu.: 3.0 3rd Qu.: 2.000 3rd Qu.: 0.0000 3rd Qu.: 0.000000
## Max. :50.0 Max. :55.000 Max. :10.0000 Max. :10.000000
## meal country market_segment distribution_channel
## Length:119390 Length:119390 Length:119390 Length:119390
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.00000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :0.00000 Median : 0.00000 Median : 0.0000
## Mean :0.03191 Mean : 0.08712 Mean : 0.1371
## 3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.00000 Max. :26.00000 Max. :72.0000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:119390 Length:119390 Min. : 0.0000 Length:119390
## Class :character Class :character 1st Qu.: 0.0000 Class :character
## Mode :character Mode :character Median : 0.0000 Mode :character
## Mean : 0.2211
## 3rd Qu.: 0.0000
## Max. :21.0000
## agent 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
## Min. :2014-10-17
## 1st Qu.:2016-02-01
## Median :2016-08-07
## Mean :2016-07-30
## 3rd Qu.:2017-02-08
## Max. :2017-09-14head(hotel_data)## # A tibble: 6 × 30
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <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
## # ℹ 25 more variables: 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>,
## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, days_in_waiting_list <dbl>, …dim(hotel_data)## [1] 119390 30names(hotel_data)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_day_of_month"
## [7] "stays_in_weekend_nights" "stays_in_week_nights"
## [9] "adults" "children"
## [11] "babies" "meal"
## [13] "country" "market_segment"
## [15] "distribution_channel" "is_repeated_guest"
## [17] "previous_cancellations" "previous_bookings_not_canceled"
## [19] "reserved_room_type" "assigned_room_type"
## [21] "booking_changes" "deposit_type"
## [23] "agent" "days_in_waiting_list"
## [25] "customer_type" "adr"
## [27] "required_car_parking_spaces" "total_of_special_requests"
## [29] "reservation_status" "reservation_status_date"head(hotel_data)## # A tibble: 6 × 30
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <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
## # ℹ 25 more variables: 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>,
## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
## # deposit_type <chr>, agent <chr>, days_in_waiting_list <dbl>, …Filling Missing Values
I am going to fill the null values in the dataset using the following methods:
- For the children and agent features I will use the median value to replace them.
- For the country feature I am going to use the mode method, which will replace the missing values with the most frequent one.
hotel_data$children <- ifelse(is.na(hotel_data$children), median(hotel_data$children, na.rm = TRUE), hotel_data$children)Explanation
In this line of code,I am addressing missing values (NA) in the
children column of the hotel_data dataframe.
The code uses the ifelse function to perform conditional
replacement. Here’s a breakdown of how it works:
- The
ifelsefunction checks each entry in thechildrencolumn. - If an entry is NA (indicating a missing value), the function
replaces it with the median value of the
childrencolumn. - The
medianfunction calculates the median, and thena.rm = TRUEparameter is used to instruct themedianfunction to ignore NA values when calculating the median.
This approach ensures that missing values in the
children column are replaced with a representative value
(the median) while preserving the overall distribution of the data.
# Convert the 'agent' column to numeric (this will convert non-numeric values to NA)
hotel_data$agent <- as.numeric(hotel_data$agent)## Warning: NAs introduced by coercion# Replace NA values in the 'agent' column with the median of the column
hotel_data$agent[is.na(hotel_data$agent)] <- median(hotel_data$agent, na.rm = TRUE)Explanation
In these lines of code, I am handling the agent column
in the hotel_data dataframe. The code aims to address
non-numeric values in the agent column and replace missing
values with an appropriate value (the median). Here’s how it works:
The first line attempts to convert the
agentcolumn to a numeric type using theas.numericfunction. This conversion may result in NA values for entries that contain non-numeric characters or missing values.The second line uses the
ifelsefunction to perform conditional replacement. It checks each entry in theagentcolumn:- If an entry is NA (indicating a missing value), it is replaced with
the median value of the
agentcolumn. - The
medianfunction calculates the median, and thena.rm = TRUEparameter is used to ensure that NA values are ignored when computing the median.
- If an entry is NA (indicating a missing value), it is replaced with
the median value of the
This approach ensures that the agent column contains
numeric values and replaces any missing or non-numeric entries with a
representative value (the median) while maintaining data integrity.
# Create a table of counts for each country
country_counts <- table(hotel_data$country)
# Find the country (or countries) with the highest count
mode_country <- names(country_counts)[country_counts == max(country_counts)]
# find which country is more frequent in our data
# Print the mode
print(mode_country)## [1] "PRT"Explanation
In this code block, I am determining the most frequently occurring
country in the country column of the
hotel_data dataframe. Here’s how it works:
The
tablefunction is used to create a frequency table of the unique values in thecountrycolumn. This table contains the count of each country’s occurrences in the dataset.The
namesfunction is applied to the result oftable, which provides the names of the countries that appear in the dataset.To identify the country or countries with the highest count (the mode), I am comparing the count of each country (retrieved from
table) to the maximum count incountry_counts.The result of this comparison will be the name(s) of the country(s) with the highest count, which represents the mode.
Finally, the mode is printed to the console, providing insight into the most frequently occurring country in the dataset.
replace null values with the most frequent value in the country column
# Calculate the mode (most frequent value)
getmode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
# Get the most frequent value for the country column in hotel_data
mode_value <- getmode(hotel_data$country[!is.na(hotel_data$country)])
# Replace NA values in the country column with the mode value
hotel_data$country[is.na(hotel_data$country)] <- mode_value
colSums(is.na(hotel_data))## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_day_of_month
## 0 0
## stays_in_weekend_nights stays_in_week_nights
## 0 0
## adults children
## 0 0
## babies meal
## 0 0
## country market_segment
## 0 0
## distribution_channel is_repeated_guest
## 0 0
## previous_cancellations previous_bookings_not_canceled
## 0 0
## reserved_room_type assigned_room_type
## 0 0
## booking_changes deposit_type
## 0 0
## agent days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0Explanation
This code block addresses missing values in the country
column of the hotel_data dataframe. Here’s the
breakdown:
First, a custom function
getmodeis defined. This function calculates the mode (most frequent value) in a vector.The
getmodefunction is applied to thecountrycolumn using thesapplyfunction. This step identifies the most common country in the dataset.Next, any NA values in the
countrycolumn are replaced with the most frequent country that was determined in the previous step. This effectively fills in missing country values with the mode.Finally,
colSums(is.na(hotel_data))is used to compute a summary count of NA values across all columns of thehotel_datadataframe. This allows you to verify that the missing values in thecountrycolumn have been successfully replaced, as the count of NA values should have decreased after this operation.
Now that I am done with the null values we can go further and ask questions in our data.
hotel_data$arrival_date_year <- as.character(hotel_data$arrival_date_year)Explanation
This code converts the arrival_date_year column from its current type (likely numeric or integer) to a character type. This might be done for consistency in data types, particularly if the year is not going to be used in calculations but rather as a categorical or grouping variable in analysis or visualizations. # Checking for miswritings
There might be miswritings in our data. Therefore I will check the object columns to see if there are errors. Like, in the column arrival_date_month might be typed “Aug” instead of “August”, or in column arrival_date_year might be typed “’15” instead of “2015” etc. But I can see that there are not miswritings here.
Subsetting Data and Exploring Unique Values
# Subset the hotel_data DataFrame with specified columns
# List the column names in the hotel_data dataframe
names(hotel_data)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_day_of_month"
## [7] "stays_in_weekend_nights" "stays_in_week_nights"
## [9] "adults" "children"
## [11] "babies" "meal"
## [13] "country" "market_segment"
## [15] "distribution_channel" "is_repeated_guest"
## [17] "previous_cancellations" "previous_bookings_not_canceled"
## [19] "reserved_room_type" "assigned_room_type"
## [21] "booking_changes" "deposit_type"
## [23] "agent" "days_in_waiting_list"
## [25] "customer_type" "adr"
## [27] "required_car_parking_spaces" "total_of_special_requests"
## [29] "reservation_status" "reservation_status_date"df <- hotel_data[, c("hotel", "arrival_date_month", "arrival_date_year", "country", "market_segment", "distribution_channel", "reserved_room_type", "assigned_room_type", "deposit_type", "customer_type", "reservation_status")]
# Iterate over the columns and print unique values
for (col in names(df)) {
cat(col, ": ", unique(df[[col]]), "\n\n")
}## hotel : Resort Hotel City Hotel
##
## arrival_date_month : July August September October November December January February March April May June
##
## arrival_date_year : 2015 2016 2017
##
## country : PRT GBR USA ESP IRL FRA NULL ROU NOR OMN ARG POL DEU BEL CHE CN GRC ITA NLD DNK RUS SWE AUS EST CZE BRA FIN MOZ BWA LUX SVN ALB IND CHN MEX MAR UKR SMR LVA PRI SRB CHL AUT BLR LTU TUR ZAF AGO ISR CYM ZMB CPV ZWE DZA KOR CRI HUN ARE TUN JAM HRV HKG IRN GEO AND GIB URY JEY CAF CYP COL GGY KWT NGA MDV VEN SVK FJI KAZ PAK IDN LBN PHL SEN SYC AZE BHR NZL THA DOM MKD MYS ARM JPN LKA CUB CMR BIH MUS COM SUR UGA BGR CIV JOR SYR SGP BDI SAU VNM PLW QAT EGY PER MLT MWI ECU MDG ISL UZB NPL BHS MAC TGO TWN DJI STP KNA ETH IRQ HND RWA KHM MCO BGD IMN TJK NIC BEN VGB TZA GAB GHA TMP GLP KEN LIE GNB MNE UMI MYT FRO MMR PAN BFA LBY MLI NAM BOL PRY BRB ABW AIA SLV DMA PYF GUY LCA ATA GTM ASM MRT NCL KIR SDN ATF SLE LAO
##
## market_segment : Direct Corporate Online TA Offline TA/TO Complementary Groups Undefined Aviation
##
## distribution_channel : Direct Corporate TA/TO Undefined GDS
##
## reserved_room_type : C A D E G F H L P B
##
## assigned_room_type : C A D E G F I B H P L K
##
## deposit_type : No Deposit Refundable Non Refund
##
## customer_type : Transient Contract Transient-Party Group
##
## reservation_status : Check-Out Canceled No-ShowExplanation
- This code begins by displaying all column names in the hotel_data dataframe to identify available variables. Then, a new dataframe df is created, which includes only a subset of those columns — specifically, columns that are likely to be relevant for further analysis. After subsetting, the code loops through each column in the new dataframe and prints out the unique values using the cat function, which helps in understanding the distinct categories or ranges of data present for each variable.
Check for logical errors
- I have three columns (children, adults, babies) that can’t be zero all the them at the same row. But we can see that there are 180 rows where all of them are 0.
filtered_data <- subset(hotel_data, children == 0 & adults == 0 & babies == 0)
filtered_data## # A tibble: 180 × 30
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <dbl> <dbl> <chr> <chr>
## 1 Resort Hotel 0 1 2015 October
## 2 Resort Hotel 0 0 2015 October
## 3 Resort Hotel 0 36 2015 November
## 4 Resort Hotel 0 165 2015 December
## 5 Resort Hotel 0 165 2015 December
## 6 Resort Hotel 1 0 2016 February
## 7 Resort Hotel 1 0 2016 November
## 8 Resort Hotel 0 31 2016 December
## 9 Resort Hotel 0 4 2017 January
## 10 Resort Hotel 0 46 2017 January
## # ℹ 170 more rows
## # ℹ 25 more variables: 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>,
## # reserved_room_type <chr>, assigned_room_type <chr>, …filtered_data <- hotel_data[hotel_data$children == 0 & hotel_data$adults == 0 & hotel_data$babies == 0, ]Explanation
- These lines of code create a subset of hotel_data containing rows where there are no guests (adults, children, or babies) associated with the booking, which is a logical inconsistency within the context of hotel bookings. The filtered_data dataframe captures all such rows. This information might be used to review the data extraction process or to understand the context of these entries.
# Identify rows with logical errors
logical_errors <- with(hotel_data, children == 0 & adults == 0 & babies == 0)
# Drop these rows
hotel_data <- hotel_data[!logical_errors, ]
# Reset index (R dataframes do not have an inherent index like pandas, but you can recreate one if needed)
hotel_data <- data.frame(hotel_data)
# View the first few rows of the updated dataframe
head(hotel_data)## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 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
## arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights adults
## 1 1 0 0 2
## 2 1 0 0 2
## 3 1 0 1 1
## 4 1 0 1 1
## 5 1 0 2 2
## 6 1 0 2 2
## children babies meal country market_segment distribution_channel
## 1 0 0 BB PRT Direct Direct
## 2 0 0 BB PRT Direct Direct
## 3 0 0 BB GBR Direct Direct
## 4 0 0 BB GBR Corporate Corporate
## 5 0 0 BB GBR Online TA TA/TO
## 6 0 0 BB GBR Online TA TA/TO
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## reserved_room_type assigned_room_type booking_changes deposit_type agent
## 1 C C 3 No Deposit 14
## 2 C C 4 No Deposit 14
## 3 A C 0 No Deposit 14
## 4 A A 0 No Deposit 304
## 5 A A 0 No Deposit 240
## 6 A A 0 No Deposit 240
## days_in_waiting_list customer_type adr required_car_parking_spaces
## 1 0 Transient 0 0
## 2 0 Transient 0 0
## 3 0 Transient 75 0
## 4 0 Transient 75 0
## 5 0 Transient 98 0
## 6 0 Transient 98 0
## total_of_special_requests reservation_status reservation_status_date
## 1 0 Check-Out 2015-07-01
## 2 0 Check-Out 2015-07-01
## 3 0 Check-Out 2015-07-02
## 4 0 Check-Out 2015-07-02
## 5 1 Check-Out 2015-07-03
## 6 1 Check-Out 2015-07-03- Let’s check if the rows with the wrong data are dropped. Now our dataset from 119390 rows has 119210 (exactly 180 rows less)
dim(hotel_data)## [1] 119210 30summary(hotel_data)## hotel is_canceled lead_time arrival_date_year
## Length:119210 Min. :0.0000 Min. : 0.0 Length:119210
## Class :character 1st Qu.:0.0000 1st Qu.: 18.0 Class :character
## Mode :character Median :0.0000 Median : 69.0 Mode :character
## Mean :0.3708 Mean :104.1
## 3rd Qu.:1.0000 3rd Qu.:161.0
## Max. :1.0000 Max. :737.0
## arrival_date_month arrival_date_day_of_month stays_in_weekend_nights
## Length:119210 Min. : 1.0 Min. : 0.0000
## Class :character 1st Qu.: 8.0 1st Qu.: 0.0000
## Mode :character Median :16.0 Median : 1.0000
## Mean :15.8 Mean : 0.9271
## 3rd Qu.:23.0 3rd Qu.: 2.0000
## Max. :31.0 Max. :19.0000
## stays_in_week_nights adults children babies
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000000
## 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 0.000000
## Median : 2.000 Median : 2.000 Median : 0.000 Median : 0.000000
## Mean : 2.499 Mean : 1.859 Mean : 0.104 Mean : 0.007961
## 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 0.000 3rd Qu.: 0.000000
## Max. :50.000 Max. :55.000 Max. :10.000 Max. :10.000000
## meal country market_segment distribution_channel
## Length:119210 Length:119210 Length:119210 Length:119210
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.0000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.0000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :0.0000 Median : 0.00000 Median : 0.0000
## Mean :0.0315 Mean : 0.08719 Mean : 0.1371
## 3rd Qu.:0.0000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.0000 Max. :26.00000 Max. :72.0000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:119210 Length:119210 Min. : 0.0000 Length:119210
## Class :character Class :character 1st Qu.: 0.0000 Class :character
## Mode :character Mode :character Median : 0.0000 Mode :character
## Mean : 0.2188
## 3rd Qu.: 0.0000
## Max. :18.0000
## agent days_in_waiting_list customer_type adr
## Min. : 1.0 Min. : 0.000 Length:119210 Min. : -6.38
## 1st Qu.: 9.0 1st Qu.: 0.000 Class :character 1st Qu.: 69.50
## Median : 14.0 Median : 0.000 Mode :character Median : 94.95
## Mean : 76.8 Mean : 2.321 Mean : 101.97
## 3rd Qu.:152.0 3rd Qu.: 0.000 3rd Qu.: 126.00
## Max. :535.0 Max. :391.000 Max. :5400.00
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:119210
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06255 Mean :0.5715
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
## reservation_status_date
## Min. :2014-10-17
## 1st Qu.:2016-02-01
## Median :2016-08-07
## Mean :2016-07-29
## 3rd Qu.:2017-02-08
## Max. :2017-09-14DESCRIPTIVE ANALYSIS / DATA ANALYSIS
- Let’s make a dataset which will include only the people that did not cancel their room reservation. And then I will analyze the data for those that did cancelled.
Creating a Subset for Non-Cancelled Bookings
# Assuming 'hotel_bookings' is your data frame
# Filter rows where 'is_canceled' is 0
not_cancelled <- hotel_data[hotel_data$is_canceled == 0, ]
# Display the first few rows
head(not_cancelled)## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 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
## arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights adults
## 1 1 0 0 2
## 2 1 0 0 2
## 3 1 0 1 1
## 4 1 0 1 1
## 5 1 0 2 2
## 6 1 0 2 2
## children babies meal country market_segment distribution_channel
## 1 0 0 BB PRT Direct Direct
## 2 0 0 BB PRT Direct Direct
## 3 0 0 BB GBR Direct Direct
## 4 0 0 BB GBR Corporate Corporate
## 5 0 0 BB GBR Online TA TA/TO
## 6 0 0 BB GBR Online TA TA/TO
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## reserved_room_type assigned_room_type booking_changes deposit_type agent
## 1 C C 3 No Deposit 14
## 2 C C 4 No Deposit 14
## 3 A C 0 No Deposit 14
## 4 A A 0 No Deposit 304
## 5 A A 0 No Deposit 240
## 6 A A 0 No Deposit 240
## days_in_waiting_list customer_type adr required_car_parking_spaces
## 1 0 Transient 0 0
## 2 0 Transient 0 0
## 3 0 Transient 75 0
## 4 0 Transient 75 0
## 5 0 Transient 98 0
## 6 0 Transient 98 0
## total_of_special_requests reservation_status reservation_status_date
## 1 0 Check-Out 2015-07-01
## 2 0 Check-Out 2015-07-01
## 3 0 Check-Out 2015-07-02
## 4 0 Check-Out 2015-07-02
## 5 1 Check-Out 2015-07-03
## 6 1 Check-Out 2015-07-03Explanation:
This code creates a new subset not_cancelled from the hotel_data dataframe, containing only the rows where the booking was not canceled (is_canceled == 0). This subset is focused on bookings that were completed. The head function is then used to display the first few rows of this new subset for a quick preview.
Let’s check if I have the right dataset. If the there are any values for columns stays_in_weekend_nights and stays_in_week_night have same value 0 or 1, then something is wrong and we have false data. Maybe something wrong with the data entry. Our outcome is False that means we are good to go.
Validating Data Consistency
# Check if 'stays_in_weekend_nights' and 'stays_in_week_nights' columns are equal in all rows
all(not_cancelled$stays_in_weekend_nights == not_cancelled$stays_in_week_nights)## [1] FALSEExplanation
This line checks whether all rows in the not_cancelled subset have the same values for stays_in_weekend_nights and stays_in_week_nights. If this returns TRUE, it might indicate an issue (like all values being 0 or 1, which could be unrealistic). In this case, the comment suggests that the output is FALSE, indicating varied and presumably realistic data.
The people that did not cancel their reservation is 63% of our data
Statistical Summary of Non-Cancelled Bookings
str(not_cancelled)## 'data.frame': 75011 obs. of 30 variables:
## $ hotel : chr "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
## $ is_canceled : num 0 0 0 0 0 0 0 0 0 0 ...
## $ lead_time : num 342 737 7 13 14 14 0 9 35 68 ...
## $ arrival_date_year : chr "2015" "2015" "2015" "2015" ...
## $ arrival_date_month : chr "July" "July" "July" "July" ...
## $ arrival_date_day_of_month : num 1 1 1 1 1 1 1 1 1 1 ...
## $ stays_in_weekend_nights : num 0 0 0 0 0 0 0 0 0 0 ...
## $ stays_in_week_nights : num 0 0 1 1 2 2 2 2 4 4 ...
## $ adults : num 2 2 1 1 2 2 2 2 2 2 ...
## $ children : num 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : num 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "BB" "BB" "BB" "BB" ...
## $ country : chr "PRT" "PRT" "GBR" "GBR" ...
## $ market_segment : chr "Direct" "Direct" "Direct" "Corporate" ...
## $ distribution_channel : chr "Direct" "Direct" "Direct" "Corporate" ...
## $ is_repeated_guest : num 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : num 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: num 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "C" "C" "A" "A" ...
## $ assigned_room_type : chr "C" "C" "C" "A" ...
## $ booking_changes : num 3 4 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : num 14 14 14 304 240 240 14 303 240 240 ...
## $ days_in_waiting_list : num 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num 0 0 75 75 98 98 107 103 145 97 ...
## $ required_car_parking_spaces : num 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : num 0 0 0 0 1 1 0 1 0 3 ...
## $ reservation_status : chr "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
## $ reservation_status_date : Date, format: "2015-07-01" "2015-07-01" ...# Get a statistical summary of the dataframe
summary(not_cancelled)## hotel is_canceled lead_time arrival_date_year
## Length:75011 Min. :0 Min. : 0.00 Length:75011
## Class :character 1st Qu.:0 1st Qu.: 9.00 Class :character
## Mode :character Median :0 Median : 45.00 Mode :character
## Mean :0 Mean : 80.08
## 3rd Qu.:0 3rd Qu.:124.00
## Max. :0 Max. :737.00
## arrival_date_month arrival_date_day_of_month stays_in_weekend_nights
## Length:75011 Min. : 1.00 Min. : 0.0000
## Class :character 1st Qu.: 8.00 1st Qu.: 0.0000
## Mode :character Median :16.00 Median : 1.0000
## Mean :15.84 Mean : 0.9281
## 3rd Qu.:23.00 3rd Qu.: 2.0000
## Max. :31.00 Max. :19.0000
## stays_in_week_nights adults children babies
## Min. : 0.000 Min. :0.000 Min. :0.0000 Min. : 0.0000
## 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.: 0.0000
## Median : 2.000 Median :2.000 Median :0.0000 Median : 0.0000
## Mean : 2.462 Mean :1.834 Mean :0.1026 Mean : 0.0104
## 3rd Qu.: 3.000 3rd Qu.:2.000 3rd Qu.:0.0000 3rd Qu.: 0.0000
## Max. :50.000 Max. :4.000 Max. :3.0000 Max. :10.0000
## meal country market_segment distribution_channel
## Length:75011 Length:75011 Length:75011 Length:75011
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.00000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :0.00000 Median : 0.00000 Median : 0.0000
## Mean :0.04273 Mean : 0.01576 Mean : 0.2031
## 3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.00000 Max. :13.00000 Max. :72.0000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:75011 Length:75011 Min. : 0.0000 Length:75011
## Class :character Class :character 1st Qu.: 0.0000 Class :character
## Mode :character Mode :character Median : 0.0000 Mode :character
## Mean : 0.2898
## 3rd Qu.: 0.0000
## Max. :18.0000
## agent days_in_waiting_list customer_type adr
## Min. : 1.00 Min. : 0.000 Length:75011 Min. : -6.38
## 1st Qu.: 9.00 1st Qu.: 0.000 Class :character 1st Qu.: 67.76
## Median : 14.00 Median : 0.000 Mode :character Median : 92.70
## Mean : 81.03 Mean : 1.588 Mean :100.17
## 3rd Qu.:171.00 3rd Qu.: 0.000 3rd Qu.:125.00
## Max. :535.00 Max. :379.000 Max. :510.00
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:75011
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :1.0000 Mode :character
## Mean :0.09941 Mean :0.7145
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
## reservation_status_date
## Min. :2015-07-01
## 1st Qu.:2016-03-11
## Median :2016-09-01
## Mean :2016-08-27
## 3rd Qu.:2017-03-12
## Max. :2017-09-14I can observe from the following table that:
- Mean time for them to check in for their room is 80 days.
- Most of them were 2 adults with no kids
- The years that our dataset is about are 2015 until 2017 and the year with the most guest is 2016 (49% of our data)
- Repeated guests are very low.
- The hotel type that was prefered was City Hotel (61% of the guests)
- August was the month with the most traffic.
- Online Travel Agents is prefered for booking, making no deposit.
- Most of the guests wanted a room type A (almost 55% of the dataset).
- Most of the guests were from Portugal (29 % of the dataset)
summary(not_cancelled)## hotel is_canceled lead_time arrival_date_year
## Length:75011 Min. :0 Min. : 0.00 Length:75011
## Class :character 1st Qu.:0 1st Qu.: 9.00 Class :character
## Mode :character Median :0 Median : 45.00 Mode :character
## Mean :0 Mean : 80.08
## 3rd Qu.:0 3rd Qu.:124.00
## Max. :0 Max. :737.00
## arrival_date_month arrival_date_day_of_month stays_in_weekend_nights
## Length:75011 Min. : 1.00 Min. : 0.0000
## Class :character 1st Qu.: 8.00 1st Qu.: 0.0000
## Mode :character Median :16.00 Median : 1.0000
## Mean :15.84 Mean : 0.9281
## 3rd Qu.:23.00 3rd Qu.: 2.0000
## Max. :31.00 Max. :19.0000
## stays_in_week_nights adults children babies
## Min. : 0.000 Min. :0.000 Min. :0.0000 Min. : 0.0000
## 1st Qu.: 1.000 1st Qu.:2.000 1st Qu.:0.0000 1st Qu.: 0.0000
## Median : 2.000 Median :2.000 Median :0.0000 Median : 0.0000
## Mean : 2.462 Mean :1.834 Mean :0.1026 Mean : 0.0104
## 3rd Qu.: 3.000 3rd Qu.:2.000 3rd Qu.:0.0000 3rd Qu.: 0.0000
## Max. :50.000 Max. :4.000 Max. :3.0000 Max. :10.0000
## meal country market_segment distribution_channel
## Length:75011 Length:75011 Length:75011 Length:75011
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.00000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.00000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :0.00000 Median : 0.00000 Median : 0.0000
## Mean :0.04273 Mean : 0.01576 Mean : 0.2031
## 3rd Qu.:0.00000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.00000 Max. :13.00000 Max. :72.0000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:75011 Length:75011 Min. : 0.0000 Length:75011
## Class :character Class :character 1st Qu.: 0.0000 Class :character
## Mode :character Mode :character Median : 0.0000 Mode :character
## Mean : 0.2898
## 3rd Qu.: 0.0000
## Max. :18.0000
## agent days_in_waiting_list customer_type adr
## Min. : 1.00 Min. : 0.000 Length:75011 Min. : -6.38
## 1st Qu.: 9.00 1st Qu.: 0.000 Class :character 1st Qu.: 67.76
## Median : 14.00 Median : 0.000 Mode :character Median : 92.70
## Mean : 81.03 Mean : 1.588 Mean :100.17
## 3rd Qu.:171.00 3rd Qu.: 0.000 3rd Qu.:125.00
## Max. :535.00 Max. :379.000 Max. :510.00
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:75011
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :1.0000 Mode :character
## Mean :0.09941 Mean :0.7145
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
## reservation_status_date
## Min. :2015-07-01
## 1st Qu.:2016-03-11
## Median :2016-09-01
## Mean :2016-08-27
## 3rd Qu.:2017-03-12
## Max. :2017-09-14Categorical Columns Analysis
# Select only categorical (non-numeric) columns
categorical_columns <- not_cancelled[sapply(not_cancelled, is.character) | sapply(not_cancelled, is.factor)]
# Get a summary of these categorical columns
summary(categorical_columns)## hotel arrival_date_year arrival_date_month meal
## Length:75011 Length:75011 Length:75011 Length:75011
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## country market_segment distribution_channel reserved_room_type
## Length:75011 Length:75011 Length:75011 Length:75011
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## assigned_room_type deposit_type customer_type reservation_status
## Length:75011 Length:75011 Length:75011 Length:75011
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :characterExplanation
- This code first isolates the categorical columns (either character or factor types) from the not_cancelled dataframe. It then provides a summary of these categorical columns, which includes counts for different categories and levels in each of these columns. This is particularly useful for understanding the distribution and frequency of different categorical variables within the non-cancelled bookings.
DATA VISUALIZATION- 1
- Let’s observe the above results in a more visual way
Creating a Pie Chart of Hotel Types
# Assuming 'not_cancelled' is your data frame
# Calculate the value counts for the 'hotel' column
hotel_counts <- table(not_cancelled$hotel)
# Create a pie chart
pie(hotel_counts, main = "Pie Chart of Hotel Types")
## Explanation - This code snippet first calculates the frequency of
each hotel type in the not_cancelled dataset using the table function.
It then creates a pie chart to visually represent these frequencies. The
pie chart, labeled “Pie Chart of Hotel Types,” shows the proportion of
each hotel type (e.g., City Hotel, Resort Hotel) in the non-cancelled
bookings, providing an immediate visual understanding of the
distribution of hotel types in the dataset.
Bar Plot of Visitors Each Month
# Load the ggplot2 package
library(ggplot2)
# Assuming 'not_cancelled' is your data frame
# Note: Ensure 'hotel' and 'arrival_date_month' are factors with the correct order
not_cancelled$arrival_date_month <- factor(not_cancelled$arrival_date_month,
levels = c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"))
# Creating the plot
ggplot(not_cancelled, aes(x = arrival_date_month, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Number of Visitors Each Month", x = "Month", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
## Explanation - Here, a bar plot is created to show the number of
visitors each month for the non-cancelled bookings. The
arrival_date_month column is first converted into an ordered factor to
ensure the months are displayed in the correct sequence. The ggplot2
package is used to create the plot, with months on the x-axis and the
count of visitors on the y-axis. The bars are color-coded by hotel type
and positioned side by side for each month (dodging position) to
facilitate comparison.
Bar Plot of Visitors Each Year
# Load the ggplot2 package
library(ggplot2)
# Assuming 'not_cancelled' is your data frame
# Note: Ensure 'hotel' and 'arrival_date_year' are factors with the correct order
not_cancelled$arrival_date_year <- factor(not_cancelled$arrival_date_year,
levels = c("2015", "2016", "2017"))
# Creating the plot
ggplot(not_cancelled, aes(x = arrival_date_year, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Number of Visitors Each Year", x = "Year", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels if needed
## Explanation - This plot shows the number of visitors each year in the
not_cancelled dataset. Similar to the previous plot, arrival_date_year
is converted to a factor to maintain the correct order. The plot
displays the count of visitors per year, separated by hotel type, to
illustrate trends or changes in visitor numbers over the years.
Histogram of Lead Time
# Load the ggplot2 package
library(ggplot2)
# Assuming 'not_cancelled' is your data frame
# Creating the histogram for 'lead_time' column
ggplot(not_cancelled, aes(x = lead_time)) +
geom_histogram(bins = 20, fill = "blue", color = "black") +
labs(title = "Histogram of Lead Time", x = "Lead Time", y = "Count") +
theme_minimal()
## Explanation - A histogram is created to visualize the distribution of
the lead_time (the number of days between booking and arrival) in the
not_cancelled dataset. The plot helps to identify common lead times and
any patterns or outliers in the booking behavior of the guests.
- Now, Let’s check from which country we the guests that checked-in. I will limit our data to the countries that had at least 200 guests, in order to plot the countries with the most guests.
Analysis of Guests per Country
# Assuming 'not_cancelled' is your data frame
# Calculate the counts for each country
country_counts <- table(not_cancelled$country)
# Convert the table to a data frame
country_counts_df <- as.data.frame(country_counts)
# Rename the columns
names(country_counts_df) <- c("country", "No of guests")
# View the resulting data frame
country_counts_df## country No of guests
## 1 ABW 2
## 2 AGO 157
## 3 AIA 1
## 4 ALB 10
## 5 AND 2
## 6 ARE 8
## 7 ARG 160
## 8 ARM 6
## 9 ASM 1
## 10 ATA 2
## 11 ATF 1
## 12 AUS 319
## 13 AUT 1033
## 14 AZE 8
## 15 BDI 1
## 16 BEL 1868
## 17 BFA 1
## 18 BGD 3
## 19 BGR 63
## 20 BHR 1
## 21 BHS 1
## 22 BIH 10
## 23 BLR 17
## 24 BOL 10
## 25 BRA 1392
## 26 BRB 4
## 27 BWA 1
## 28 CAF 5
## 29 CHE 1298
## 30 CHL 49
## 31 CHN 537
## 32 CIV 4
## 33 CMR 10
## 34 CN 1025
## 35 COL 48
## 36 COM 2
## 37 CPV 12
## 38 CRI 18
## 39 CUB 8
## 40 CYM 1
## 41 CYP 40
## 42 CZE 134
## 43 DEU 6067
## 44 DJI 1
## 45 DMA 1
## 46 DNK 326
## 47 DOM 6
## 48 DZA 82
## 49 ECU 19
## 50 EGY 21
## 51 ESP 6383
## 52 EST 65
## 53 ETH 2
## 54 FIN 377
## 55 FRA 8468
## 56 FRO 1
## 57 GAB 2
## 58 GBR 9668
## 59 GEO 7
## 60 GHA 2
## 61 GIB 7
## 62 GNB 8
## 63 GRC 93
## 64 GTM 4
## 65 GUY 1
## 66 HKG 3
## 67 HRV 75
## 68 HUN 153
## 69 IDN 11
## 70 IND 116
## 71 IRL 2542
## 72 IRN 59
## 73 IRQ 14
## 74 ISL 53
## 75 ISR 500
## 76 ITA 2428
## 77 JAM 6
## 78 JOR 18
## 79 JPN 169
## 80 KAZ 14
## 81 KEN 4
## 82 KIR 1
## 83 KNA 2
## 84 KOR 78
## 85 KWT 10
## 86 LAO 2
## 87 LBN 22
## 88 LBY 8
## 89 LCA 1
## 90 LIE 2
## 91 LKA 7
## 92 LTU 74
## 93 LUX 177
## 94 LVA 46
## 95 MAC 1
## 96 MAR 150
## 97 MCO 3
## 98 MDG 1
## 99 MDV 3
## 100 MEX 75
## 101 MKD 8
## 102 MLI 1
## 103 MLT 13
## 104 MMR 1
## 105 MNE 3
## 106 MOZ 48
## 107 MRT 1
## 108 MUS 6
## 109 MWI 2
## 110 MYS 25
## 111 NAM 1
## 112 NCL 1
## 113 NGA 13
## 114 NLD 1716
## 115 NOR 426
## 116 NPL 1
## 117 NULL 421
## 118 NZL 68
## 119 OMN 14
## 120 PAK 5
## 121 PAN 9
## 122 PER 23
## 123 PHL 15
## 124 PLW 1
## 125 POL 703
## 126 PRI 10
## 127 PRT 20977
## 128 PRY 4
## 129 PYF 1
## 130 QAT 4
## 131 ROU 366
## 132 RUS 391
## 133 RWA 2
## 134 SAU 15
## 135 SDN 1
## 136 SEN 3
## 137 SGP 22
## 138 SLE 1
## 139 SLV 2
## 140 SMR 1
## 141 SRB 98
## 142 STP 2
## 143 SUR 5
## 144 SVK 41
## 145 SVN 41
## 146 SWE 793
## 147 SYC 1
## 148 SYR 3
## 149 TGO 2
## 150 THA 41
## 151 TJK 1
## 152 TMP 2
## 153 TUN 20
## 154 TUR 146
## 155 TWN 37
## 156 TZA 2
## 157 UGA 2
## 158 UKR 48
## 159 URY 23
## 160 USA 1592
## 161 UZB 2
## 162 VEN 14
## 163 VNM 6
## 164 ZAF 49
## 165 ZMB 1
## 166 ZWE 2# Filter out rows where 'No of guests' is less than 200
country_counts_filtered <- country_counts_df[country_counts_df$`No of guests` >= 200, ]
# View the filtered data frame
country_counts_filtered## country No of guests
## 12 AUS 319
## 13 AUT 1033
## 16 BEL 1868
## 25 BRA 1392
## 29 CHE 1298
## 31 CHN 537
## 34 CN 1025
## 43 DEU 6067
## 46 DNK 326
## 51 ESP 6383
## 54 FIN 377
## 55 FRA 8468
## 58 GBR 9668
## 71 IRL 2542
## 75 ISR 500
## 76 ITA 2428
## 114 NLD 1716
## 115 NOR 426
## 117 NULL 421
## 125 POL 703
## 127 PRT 20977
## 131 ROU 366
## 132 RUS 391
## 146 SWE 793
## 160 USA 1592Explanation
- This code calculates the number of guests from each country in the not_cancelled dataset and converts the resulting table into a dataframe for easier manipulation. The dataframe is then filtered to include only countries with at least 200 guests, focusing the analysis on the countries with the most guests.
Bar Plot of Number of Guests per Country
# Load the ggplot2 package
library(ggplot2)
# Assuming 'country_counts_df' is your data frame
# Creating the bar plot
ggplot(data = country_counts_df, aes(x = country, y = `No of guests`)) +
geom_bar(stat = "identity", fill = "blue", color = "black") +
labs(title = "Number of Guests per Country", x = "Country", y = "No of Guests") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis labels for better readability
## Explanation - A bar plot is created to visualize the number of guests
from each country in the not_cancelled dataset. The x-axis represents
different countries, and the y-axis shows the count of guests. The plot
provides a clear visual comparison of the guest distribution across
different countries, highlighting which countries contribute the most to
the hotel’s clientele. The x-axis labels are rotated for better
readability.
DATA ANALYSIS FOR CANCELLED ROOM
# Assuming 'hotel_bookings' is your data frame
# Filter rows where 'is_canceled' equals 1
cancelled <- hotel_data[hotel_data$is_canceled == 1, ]
# Display the first few rows of the cancelled subset
head(cancelled)## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 9 Resort Hotel 1 85 2015 July
## 10 Resort Hotel 1 75 2015 July
## 11 Resort Hotel 1 23 2015 July
## 28 Resort Hotel 1 60 2015 July
## 33 Resort Hotel 1 96 2015 July
## 35 Resort Hotel 1 45 2015 July
## arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights
## 9 1 0 3
## 10 1 0 3
## 11 1 0 4
## 28 1 2 5
## 33 1 2 8
## 35 2 1 3
## adults children babies meal country market_segment distribution_channel
## 9 2 0 0 BB PRT Online TA TA/TO
## 10 2 0 0 HB PRT Offline TA/TO TA/TO
## 11 2 0 0 BB PRT Online TA TA/TO
## 28 2 0 0 BB PRT Online TA TA/TO
## 33 2 0 0 BB PRT Direct Direct
## 35 3 0 0 BB PRT Online TA TA/TO
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 28 0 0 0
## 33 0 0 0
## 35 0 0 0
## reserved_room_type assigned_room_type booking_changes deposit_type agent
## 9 A A 0 No Deposit 240
## 10 D D 0 No Deposit 15
## 11 E E 0 No Deposit 240
## 28 E E 0 No Deposit 240
## 33 E E 0 No Deposit 14
## 35 D D 0 No Deposit 241
## days_in_waiting_list customer_type adr required_car_parking_spaces
## 9 0 Transient 82.0 0
## 10 0 Transient 105.5 0
## 11 0 Transient 123.0 0
## 28 0 Transient 107.0 0
## 33 0 Transient 108.3 0
## 35 0 Transient 108.8 0
## total_of_special_requests reservation_status reservation_status_date
## 9 1 Canceled 2015-05-06
## 10 0 Canceled 2015-04-22
## 11 0 Canceled 2015-06-23
## 28 2 Canceled 2015-05-11
## 33 2 Canceled 2015-05-29
## 35 1 Canceled 2015-05-19Explanation:
This code creates a new subset cancelled from the hotel_data dataframe, containing only the rows where the booking was canceled (is_canceled == 1). The head function is used to display the first few rows of this subset for verification.
Checking if our dataset is right. By checking the column “reservation_status” I can see that it has only values “Canceled” and “No-show”. Therefore, our dataset is right, having only the cancelled data.
# Get unique values in the 'reservation_status' column of the 'cancelled' dataframe
unique_values <- unique(cancelled$reservation_status)
# Print the unique values
unique_values## [1] "Canceled" "No-Show"Explanation
- The unique function is used to retrieve and print the unique values from the reservation_status column of the cancelled dataframe. This step is crucial for validating that the dataset contains only cancelled reservations, which should show statuses like “Canceled” or “No-show”.
# Check if 'stays_in_weekend_nights' and 'stays_in_week_nights' columns are equal in all rows
are_equal <- all(cancelled$stays_in_weekend_nights == cancelled$stays_in_week_nights)
# Print the result
are_equal## [1] FALSEExplanation
- This code checks whether all bookings in the cancelled subset have the same number of ‘stays_in_weekend_nights’ and ‘stays_in_week_nights’. This is to ensure there is no inconsistency in the data where a booking shows zero nights stayed but is marked as cancelled.
Structural and Statistical Summary of Cancelled Bookings
# Get the structure of the dataframe 'cancelled'
str(cancelled)## 'data.frame': 44199 obs. of 30 variables:
## $ hotel : chr "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
## $ is_canceled : num 1 1 1 1 1 1 1 1 1 1 ...
## $ lead_time : num 85 75 23 60 96 45 40 43 45 47 ...
## $ arrival_date_year : chr "2015" "2015" "2015" "2015" ...
## $ arrival_date_month : chr "July" "July" "July" "July" ...
## $ arrival_date_day_of_month : num 1 1 1 1 1 2 2 2 2 2 ...
## $ stays_in_weekend_nights : num 0 0 0 2 2 1 1 1 2 2 ...
## $ stays_in_week_nights : num 3 3 4 5 8 3 3 3 3 5 ...
## $ adults : num 2 2 2 2 2 3 3 3 2 2 ...
## $ children : num 0 0 0 0 0 0 0 0 0 2 ...
## $ babies : num 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "BB" "HB" "BB" "BB" ...
## $ country : chr "PRT" "PRT" "PRT" "PRT" ...
## $ market_segment : chr "Online TA" "Offline TA/TO" "Online TA" "Online TA" ...
## $ distribution_channel : chr "TA/TO" "TA/TO" "TA/TO" "TA/TO" ...
## $ is_repeated_guest : num 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : num 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: num 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "A" "D" "E" "E" ...
## $ assigned_room_type : chr "A" "D" "E" "E" ...
## $ booking_changes : num 0 0 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : num 240 15 240 240 14 241 241 241 241 240 ...
## $ days_in_waiting_list : num 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num 82 106 123 107 108 ...
## $ required_car_parking_spaces : num 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : num 1 0 0 2 2 1 1 0 0 0 ...
## $ reservation_status : chr "Canceled" "Canceled" "Canceled" "Canceled" ...
## $ reservation_status_date : Date, format: "2015-05-06" "2015-04-22" ...# Optionally, you can also get a statistical summary of the dataframe
summary(cancelled)## hotel is_canceled lead_time arrival_date_year
## Length:44199 Min. :1 Min. : 0.0 Length:44199
## Class :character 1st Qu.:1 1st Qu.: 49.0 Class :character
## Mode :character Median :1 Median :113.0 Mode :character
## Mean :1 Mean :144.9
## 3rd Qu.:1 3rd Qu.:214.0
## Max. :1 Max. :629.0
## arrival_date_month arrival_date_day_of_month stays_in_weekend_nights
## Length:44199 Min. : 1.00 Min. : 0.0000
## Class :character 1st Qu.: 8.00 1st Qu.: 0.0000
## Mode :character Median :16.00 Median : 1.0000
## Mean :15.73 Mean : 0.9253
## 3rd Qu.:23.00 3rd Qu.: 2.0000
## Max. :31.00 Max. :16.0000
## stays_in_week_nights adults children babies
## Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. :0.000000
## 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 0.0000 1st Qu.:0.000000
## Median : 2.000 Median : 2.000 Median : 0.0000 Median :0.000000
## Mean : 2.562 Mean : 1.903 Mean : 0.1066 Mean :0.003824
## 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 0.0000 3rd Qu.:0.000000
## Max. :40.000 Max. :55.000 Max. :10.0000 Max. :2.000000
## meal country market_segment distribution_channel
## Length:44199 Length:44199 Length:44199 Length:44199
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.00000 Min. : 0.0000 Min. : 0.00000
## 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.00000
## Median :0.00000 Median : 0.0000 Median : 0.00000
## Mean :0.01244 Mean : 0.2084 Mean : 0.02514
## 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.00000
## Max. :1.00000 Max. :26.0000 Max. :58.00000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:44199 Length:44199 Min. : 0.00000 Length:44199
## Class :character Class :character 1st Qu.: 0.00000 Class :character
## Mode :character Mode :character Median : 0.00000 Mode :character
## Mean : 0.09833
## 3rd Qu.: 0.00000
## Max. :16.00000
## agent days_in_waiting_list customer_type adr
## Min. : 1.00 Min. : 0.000 Length:44199 Min. : 0.0
## 1st Qu.: 9.00 1st Qu.: 0.000 Class :character 1st Qu.: 72.8
## Median : 14.00 Median : 0.000 Mode :character Median : 96.3
## Mean : 69.63 Mean : 3.566 Mean : 105.0
## 3rd Qu.: 96.00 3rd Qu.: 0.000 3rd Qu.: 127.7
## Max. :531.00 Max. :391.000 Max. :5400.0
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0 Min. :0.0000 Length:44199
## 1st Qu.:0 1st Qu.:0.0000 Class :character
## Median :0 Median :0.0000 Mode :character
## Mean :0 Mean :0.3289
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :5.0000
## reservation_status_date
## Min. :2014-10-17
## 1st Qu.:2015-12-18
## Median :2016-06-09
## Mean :2016-06-10
## 3rd Qu.:2017-01-03
## Max. :2017-08-31Explanation
- The str function is used to display the structure of the cancelled dataframe, showing details such as column types and brief data previews. The summary function provides a detailed statistical summary, including mean, median, min, max values, and counts for different categories in each column.
I can observe from the following table that:
- Mean time for them to check in for their room is 144 days. (That means that if the room is booked to many days prior the chech-in day, probably will be cancelled.)
- Most of them were 2 adults with no kids
- Most cancellations were made for City Hotel (75% of the cancellations)
- August had the most cancellations.
- Most cancellations were made from Portugal (62 % of the cancellations)
# Get a statistical summary of the 'cancelled' data frame
summary(cancelled)## hotel is_canceled lead_time arrival_date_year
## Length:44199 Min. :1 Min. : 0.0 Length:44199
## Class :character 1st Qu.:1 1st Qu.: 49.0 Class :character
## Mode :character Median :1 Median :113.0 Mode :character
## Mean :1 Mean :144.9
## 3rd Qu.:1 3rd Qu.:214.0
## Max. :1 Max. :629.0
## arrival_date_month arrival_date_day_of_month stays_in_weekend_nights
## Length:44199 Min. : 1.00 Min. : 0.0000
## Class :character 1st Qu.: 8.00 1st Qu.: 0.0000
## Mode :character Median :16.00 Median : 1.0000
## Mean :15.73 Mean : 0.9253
## 3rd Qu.:23.00 3rd Qu.: 2.0000
## Max. :31.00 Max. :16.0000
## stays_in_week_nights adults children babies
## Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. :0.000000
## 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 0.0000 1st Qu.:0.000000
## Median : 2.000 Median : 2.000 Median : 0.0000 Median :0.000000
## Mean : 2.562 Mean : 1.903 Mean : 0.1066 Mean :0.003824
## 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 0.0000 3rd Qu.:0.000000
## Max. :40.000 Max. :55.000 Max. :10.0000 Max. :2.000000
## meal country market_segment distribution_channel
## Length:44199 Length:44199 Length:44199 Length:44199
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.00000 Min. : 0.0000 Min. : 0.00000
## 1st Qu.:0.00000 1st Qu.: 0.0000 1st Qu.: 0.00000
## Median :0.00000 Median : 0.0000 Median : 0.00000
## Mean :0.01244 Mean : 0.2084 Mean : 0.02514
## 3rd Qu.:0.00000 3rd Qu.: 0.0000 3rd Qu.: 0.00000
## Max. :1.00000 Max. :26.0000 Max. :58.00000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:44199 Length:44199 Min. : 0.00000 Length:44199
## Class :character Class :character 1st Qu.: 0.00000 Class :character
## Mode :character Mode :character Median : 0.00000 Mode :character
## Mean : 0.09833
## 3rd Qu.: 0.00000
## Max. :16.00000
## agent days_in_waiting_list customer_type adr
## Min. : 1.00 Min. : 0.000 Length:44199 Min. : 0.0
## 1st Qu.: 9.00 1st Qu.: 0.000 Class :character 1st Qu.: 72.8
## Median : 14.00 Median : 0.000 Mode :character Median : 96.3
## Mean : 69.63 Mean : 3.566 Mean : 105.0
## 3rd Qu.: 96.00 3rd Qu.: 0.000 3rd Qu.: 127.7
## Max. :531.00 Max. :391.000 Max. :5400.0
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0 Min. :0.0000 Length:44199
## 1st Qu.:0 1st Qu.:0.0000 Class :character
## Median :0 Median :0.0000 Mode :character
## Mean :0 Mean :0.3289
## 3rd Qu.:0 3rd Qu.:0.0000
## Max. :0 Max. :5.0000
## reservation_status_date
## Min. :2014-10-17
## 1st Qu.:2015-12-18
## Median :2016-06-09
## Mean :2016-06-10
## 3rd Qu.:2017-01-03
## Max. :2017-08-31Analyzing Categorical Columns
# Select only non-numeric (categorical) columns from the 'cancelled' data frame
categorical_columns <- cancelled[sapply(cancelled, is.character) | sapply(cancelled, is.factor)]
# Get a summary of these categorical columns
summary(categorical_columns)## hotel arrival_date_year arrival_date_month meal
## Length:44199 Length:44199 Length:44199 Length:44199
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## country market_segment distribution_channel reserved_room_type
## Length:44199 Length:44199 Length:44199 Length:44199
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## assigned_room_type deposit_type customer_type reservation_status
## Length:44199 Length:44199 Length:44199 Length:44199
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :characterExplanation
- This code isolates the categorical columns (either character or factor types) from the cancelled dataframe and provides a summary of these columns. This is useful for understanding the distribution and frequency of different categorical variables within the cancelled bookings. # COMPARE VISUALY THE GUESTS WHO CANCELLED THE BOOKING AND THOSE WHO CHECKED IN THEIR RESERVATION
Visual Comparison of Cancelled and Non-Cancelled Bookings
Creating Pie Charts for Hotel Distribution
# Assuming 'not_cancelled' and 'cancelled' are your data frames
# Set up the plotting area for two pie charts side by side
par(mfrow = c(1, 2))
# Pie chart for 'hotel' distribution in not_cancelled data
hotel_counts_not_cancelled <- table(not_cancelled$hotel)
pie(hotel_counts_not_cancelled,
labels = paste(names(hotel_counts_not_cancelled), "\n", prop.table(hotel_counts_not_cancelled) * 100, "%"),
main = "Guests vs Hotel Type")
# Pie chart for 'hotel' distribution in cancelled data
hotel_counts_cancelled <- table(cancelled$hotel)
pie(hotel_counts_cancelled,
labels = paste(names(hotel_counts_cancelled), "\n", prop.table(hotel_counts_cancelled) * 100, "%"),
main = "Cancellation vs Hotel Type")
## Explanation - This code creates two side-by-side pie charts to
visually compare the distribution of hotel types between non-cancelled
and cancelled bookings. The table function is used to calculate the
frequency of each hotel type in both subsets. The pie charts provide a
clear visual representation of the proportion of each hotel type in the
two subsets, highlighting differences in hotel preference between guests
who cancelled and those who did not.
Bar Plots Comparing Yearly and Monthly Data
# Load the necessary libraries
library(ggplot2)
library(gridExtra)## Warning: package 'gridExtra' was built under R version 4.3.2##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine# Assuming 'not_cancelled' and 'cancelled' are your data frames
# Note: Ensure 'hotel' and 'arrival_date_year' are factors with the correct order
not_cancelled$arrival_date_year <- factor(not_cancelled$arrival_date_year,
levels = c("2015", "2016", "2017"))
cancelled$arrival_date_year <- factor(cancelled$arrival_date_year,
levels = c("2015", "2016", "2017"))
# Creating the plots
plot1 <- ggplot(not_cancelled, aes(x = arrival_date_year, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Number of Visitors Each Year", x = "Year", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot2 <- ggplot(cancelled, aes(x = arrival_date_year, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Number of Cancellations Each Year", x = "Year", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Combine the plots side by side
grid.arrange(plot1, plot2, ncol = 2)
# Load the necessary libraries
library(ggplot2)
library(patchwork)## Warning: package 'patchwork' was built under R version 4.3.2# Assuming 'not_cancelled' and 'cancelled' are your data frames
# Note: Ensure 'hotel' and 'arrival_date_month' are factors with the correct order
not_cancelled$arrival_date_month <- factor(not_cancelled$arrival_date_month,
levels = c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"))
cancelled$arrival_date_month <- factor(cancelled$arrival_date_month,
levels = c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"))
# Creating the plots
plot1 <- ggplot(not_cancelled, aes(x = arrival_date_month, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Number of Visitors Each Month", x = "Month", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot2 <- ggplot(cancelled, aes(x = arrival_date_month, fill = hotel)) +
geom_bar(position = "dodge") +
labs(title = "Number of Cancellations Each Month", x = "Month", y = "Count") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Combine the plots side by side with shared y-axis
plot1 + plot2 + plot_layout(guides = 'collect') & theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Explanation - These code segments create bar plots to compare the
number of visitors and cancellations by year and month. The data is
first converted to factors with the correct order. ggplot2 is used to
create the plots, which are then combined side by side for easy
comparison. The plots offer visual insights into trends and patterns in
booking and cancellation behaviors over time.
Displaying First Few Rows of Non-Cancelled Data
# Assuming 'data' is your data frame and 'is_canceled' is one of its columns
# Filter rows where 'is_canceled' equals 0
df <- hotel_data[hotel_data$is_canceled == 0, ]
# Display the first few rows of the 'df' subset
head(df)## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 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
## arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights adults
## 1 1 0 0 2
## 2 1 0 0 2
## 3 1 0 1 1
## 4 1 0 1 1
## 5 1 0 2 2
## 6 1 0 2 2
## children babies meal country market_segment distribution_channel
## 1 0 0 BB PRT Direct Direct
## 2 0 0 BB PRT Direct Direct
## 3 0 0 BB GBR Direct Direct
## 4 0 0 BB GBR Corporate Corporate
## 5 0 0 BB GBR Online TA TA/TO
## 6 0 0 BB GBR Online TA TA/TO
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## reserved_room_type assigned_room_type booking_changes deposit_type agent
## 1 C C 3 No Deposit 14
## 2 C C 4 No Deposit 14
## 3 A C 0 No Deposit 14
## 4 A A 0 No Deposit 304
## 5 A A 0 No Deposit 240
## 6 A A 0 No Deposit 240
## days_in_waiting_list customer_type adr required_car_parking_spaces
## 1 0 Transient 0 0
## 2 0 Transient 0 0
## 3 0 Transient 75 0
## 4 0 Transient 75 0
## 5 0 Transient 98 0
## 6 0 Transient 98 0
## total_of_special_requests reservation_status reservation_status_date
## 1 0 Check-Out 2015-07-01
## 2 0 Check-Out 2015-07-01
## 3 0 Check-Out 2015-07-02
## 4 0 Check-Out 2015-07-02
## 5 1 Check-Out 2015-07-03
## 6 1 Check-Out 2015-07-03DATA VISUALIZATION- 2
What is Average Daily Rate (ADR)?
Average daily rate (ADR), one of the three key hotel performance indicators (along with occupancy and RevPAR), is the measure of the average paid for rooms sold in a given time period. The metric covers only revenue-generating guestrooms.
ADR is calculated by dividing room revenue by rooms sold. The metric is of course applicable for any currency.
ADR = Room Revenue/Rooms Sold
If I sum the adr values I will have the profit of the hotels. Let’s see how the total adr works with different parameters. Like:
- How much meal type affects the profit of the hotels
- Which room type is the most profitable?
- Which customer type is more profitable?
- Which year we have the best profit?
library(dplyr)
library(ggplot2)
library(gridExtra)
# Assuming your data frame 'df' is already loaded
# Create grouped dataframes
df1 <- df %>% group_by(arrival_date_year) %>% summarise(adr = sum(adr))
df2 <- df %>% group_by(market_segment) %>% summarise(adr = sum(adr))
df3 <- df %>% group_by(is_repeated_guest) %>% summarise(adr = sum(adr))
df4 <- df %>% group_by(meal) %>% summarise(adr = sum(adr))
df5 <- df %>% group_by(customer_type) %>% summarise(adr = sum(adr))
df6 <- df %>% group_by(assigned_room_type) %>% summarise(adr = sum(adr))
# Function to create a horizontal bar plot
create_barh_plot <- function(df, x, y, title) {
ggplot(df, aes_string(x = x, y = y)) +
geom_bar(stat = "identity", fill = "steelblue", color = "black") +
coord_flip() + # Flipping the coordinates for horizontal bars
labs(title = title, x = "Adr", y = "") +
theme_minimal() +
theme(axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, hjust = 0.5))
}
# Create each plot
plot1 <- create_barh_plot(df1, "adr", "arrival_date_year", "Year")## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated. plot2 <- create_barh_plot(df2, "adr", "market_segment", "Market Segment")
plot3 <- create_barh_plot(df3, "adr", "is_repeated_guest", "Repeated Guest")
plot4 <- create_barh_plot(df4, "adr", "meal", "Meal")
plot5 <- create_barh_plot(df5, "adr", "customer_type", "Customer Type")
plot6 <- create_barh_plot(df6, "adr", "assigned_room_type", "Room Type")
# Arrange the plots in a 3x2 grid
grid.arrange(plot1, plot2, plot3, plot4, plot5, plot6, ncol = 2, nrow = 3)
## Explanation - This code creates a series of horizontal bar plots to
analyze how the ADR varies across different parameters like year, market
segment, customer type, meal type, etc. The create_barh_plot function
simplifies the process of creating each plot. The plots are then
arranged in a 3x2 grid, providing a comprehensive visual comparison
across different categories.
ADR for different months
- It would be interesting to compare adr values for each month, for different type of hotels.
library(dplyr)
# Assuming 'df' is your data frame
# Create subsets based on hotel type
df_resort <- filter(df, hotel == 'Resort Hotel')
df_city <- filter(df, hotel == 'City Hotel')
# Calculate average adr per month for resort and city hotels
resort_hotel <- df_resort %>%
group_by(arrival_date_month) %>%
summarise(adr = mean(adr, na.rm = TRUE)) %>%
ungroup() %>%
arrange(match(arrival_date_month, month.abb))
city_hotel <- df_city %>%
group_by(arrival_date_month) %>%
summarise(adr = mean(adr, na.rm = TRUE)) %>%
ungroup() %>%
arrange(match(arrival_date_month, month.abb))
# Calculate average adr per country for resort and city hotels
resort_hotel_2 <- df_resort %>%
group_by(country) %>%
summarise(adr = mean(adr, na.rm = TRUE)) %>%
ungroup()
city_hotel_2 <- df_city %>%
group_by(country) %>%
summarise(adr = mean(adr, na.rm = TRUE)) %>%
ungroup()library(dplyr)
# Assuming 'resort_hotel' and 'city_hotel' data frames are already created
# Merge the data frames
hotel_type <- merge(resort_hotel, city_hotel, by = "arrival_date_month")
# Rename the columns
names(hotel_type) <- c("month", "price_for_resort", "price_for_city_hotel")
# View the merged data frame
hotel_type## month price_for_resort price_for_city_hotel
## 1 April 75.86782 111.96227
## 2 August 181.20589 118.67460
## 3 December 68.41010 88.40186
## 4 February 54.14748 86.52006
## 5 January 48.76113 82.33098
## 6 July 150.12253 115.81802
## 7 June 107.97485 117.87436
## 8 March 57.05684 90.65853
## 9 May 76.65756 120.66983
## 10 November 48.70629 86.94659
## 11 October 61.77545 102.00467
## 12 September 96.41686 112.77658Explanation
- This segment of code is dedicated to comparing the ADR for Resort and City Hotels by month. It first creates subsets for each type of hotel. Then, it calculates and summarizes the average ADR per month for each hotel type. The data frames for each hotel type are merged for comparison, and the columns are renamed for clarity. This comparison helps in understanding how ADR varies between different types of hotels and across different months of the year.
# Assuming 'hotel_type' is your data frame and it has a column named 'month'
# Define the correct order of months
sort_order <- c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December")
# Convert 'month' into a factor with levels in the correct order
hotel_type$month <- factor(hotel_type$month, levels = sort_order)
# Sort the dataframe based on the 'month' factor
hotel_type_sorted <- hotel_type[order(hotel_type$month), ]
# Reset row names (optional)
rownames(hotel_type_sorted) <- NULL
# View the sorted dataframe
hotel_type_sorted## month price_for_resort price_for_city_hotel
## 1 January 48.76113 82.33098
## 2 February 54.14748 86.52006
## 3 March 57.05684 90.65853
## 4 April 75.86782 111.96227
## 5 May 76.65756 120.66983
## 6 June 107.97485 117.87436
## 7 July 150.12253 115.81802
## 8 August 181.20589 118.67460
## 9 September 96.41686 112.77658
## 10 October 61.77545 102.00467
## 11 November 48.70629 86.94659
## 12 December 68.41010 88.40186- Prices for city hotel are ranged between 70 to 100. But for resort hotel the range is different enough, from 40 to 180. There are months like January and November with very low prices and months like July and August with very high prices.
Bar Plot of ADR by Month for Different Hotel Types
library(ggplot2)
# Assuming 'hotel_type_sorted' is your data frame and it's already sorted by month
# and contains columns 'month', 'price_for_resort', and 'price_for_city_hotel'
# Reshape the data from wide to long format for ggplot2
long_format <- reshape2::melt(hotel_type_sorted, id.vars = "month",
measure.vars = c("price_for_resort", "price_for_city_hotel"))
# Create the bar plot
ggplot(long_format, aes(x = month, y = value, fill = variable)) +
geom_bar(stat = "identity", position = position_dodge()) +
labs(title = "Prices for Rooms per Month", x = "Month", y = "Price") +
theme_minimal() +
scale_fill_discrete(name = "Hotel Type", labels = c("Resort Hotel", "City Hotel"))
## Explanation - This code creates a bar plot that compares the ADR
(Average Daily Rate) for Resort and City Hotels across different months.
The data is first reshaped into a long format suitable for ggplot2,
making it easier to plot the two types of hotel prices side by side for
each month. The plot visually illustrates how prices vary between hotel
types and throughout the year, highlighting months with higher or lower
rates.
ADR for different countries
- It would be an interesting visualization to plot our data in a world map to see and compare how much our hotel room types differs from country to country.
library(dplyr)
# Assuming 'resort_hotel_2' and 'city_hotel_2' are already created and have a column named 'country'
# Merge the data frames
hotel_type_2 <- merge(resort_hotel_2, city_hotel_2, by = "country")
# Rename the columns
names(hotel_type_2) <- c("country", "price_for_resort", "price_for_city_hotel")
# View the merged data frame
hotel_type_2## country price_for_resort price_for_city_hotel
## 1 AGO 121.23235 110.94243
## 2 ALB 71.07500 81.01125
## 3 ARE 204.44333 80.00000
## 4 ARG 115.45854 113.19920
## 5 ARM 52.35000 111.66250
## 6 AUS 114.72083 116.76239
## 7 AUT 81.79784 107.28782
## 8 AZE 187.80000 100.35500
## 9 BEL 91.43267 116.74986
## 10 BGR 84.70000 111.49172
## 11 BIH 50.00000 114.49444
## 12 BLR 67.56750 98.37769
## 13 BRA 108.65891 107.38630
## 14 CAF 56.30000 114.71000
## 15 CHE 121.04619 115.95673
## 16 CHL 126.62214 114.19886
## 17 CHN 81.11592 112.56265
## 18 CIV 58.50000 123.00000
## 19 CMR 45.43000 99.05125
## 20 CN 101.35669 107.65134
## 21 COL 109.09500 124.20306
## 22 COM 71.61000 259.00000
## 23 CPV 64.00000 96.05000
## 24 CRI 36.26000 108.90937
## 25 CUB 69.50000 129.23250
## 26 CYP 92.00000 90.69382
## 27 CZE 106.28435 107.29387
## 28 DEU 76.89230 106.44052
## 29 DNK 99.17933 120.15910
## 30 DOM 97.76667 109.55667
## 31 DZA 134.98286 107.00800
## 32 ECU 53.16500 131.79706
## 33 EGY 122.89000 112.27000
## 34 ESP 110.85082 110.02880
## 35 EST 94.80345 118.14278
## 36 FIN 79.59370 113.66459
## 37 FRA 91.93590 108.66598
## 38 GBR 76.73502 112.33517
## 39 GEO 221.59250 85.43333
## 40 GRC 72.62500 112.49729
## 41 HKG 92.00000 115.00000
## 42 HRV 109.49556 105.52955
## 43 HUN 120.39316 109.61522
## 44 IDN 115.98333 117.38125
## 45 IND 104.64727 107.79434
## 46 IRL 86.50934 112.36635
## 47 IRN 109.31000 118.31389
## 48 ISL 147.45833 106.16617
## 49 ISR 103.00187 103.88401
## 50 ITA 99.90995 113.03734
## 51 JAM 99.46200 137.40000
## 52 JOR 74.85000 99.08125
## 53 JPN 99.44333 121.70375
## 54 KAZ 148.51250 124.72000
## 55 KOR 66.64000 110.12750
## 56 KWT 136.59000 107.13500
## 57 LBN 139.66667 126.34474
## 58 LKA 33.30000 96.83333
## 59 LTU 98.96889 98.31345
## 60 LUX 139.23685 125.99398
## 61 LVA 71.87103 108.62941
## 62 MAR 145.75923 106.43928
## 63 MDV 133.50000 75.00000
## 64 MEX 85.61667 119.88391
## 65 MKD 108.75000 75.00000
## 66 MLT 43.90000 108.77727
## 67 MOZ 142.00000 101.10578
## 68 MUS 98.00000 80.91400
## 69 MYS 59.23000 96.73867
## 70 NGA 116.46875 106.89000
## 71 NLD 80.57229 113.98853
## 72 NOR 102.09350 121.31617
## 73 NULL 60.27993 87.50000
## 74 NZL 90.58357 112.76778
## 75 OMN 81.62091 143.00000
## 76 PAK 171.90500 123.66667
## 77 PER 234.00000 120.13136
## 78 PHL 164.76143 137.37500
## 79 POL 85.93793 112.94130
## 80 PRI 66.05111 120.00000
## 81 PRT 90.58931 90.84796
## 82 QAT 0.00000 103.00000
## 83 ROU 121.56407 108.41584
## 84 RUS 113.54930 109.84589
## 85 SAU 171.00000 121.95357
## 86 SGP 120.42750 96.25444
## 87 SRB 86.32714 99.97846
## 88 SUR 91.50000 99.00000
## 89 SVK 91.88222 118.71094
## 90 SVN 137.39500 110.38581
## 91 SWE 100.42126 114.69240
## 92 SYR 54.00000 120.50000
## 93 TGO 152.00000 59.50000
## 94 THA 105.83333 100.01371
## 95 TUR 131.87500 102.75315
## 96 TWN 156.50000 114.87760
## 97 UGA 43.00000 126.00000
## 98 UKR 53.40870 120.46760
## 99 URY 137.25800 125.64722
## 100 USA 128.53486 116.09002
## 101 UZB 60.00000 138.50000
## 102 VEN 55.20000 111.96750
## 103 VNM 171.37500 100.07250
## 104 ZAF 118.97231 105.29889Explanation
This snippet merges two data frames - resort_hotel_2 and city_hotel_2 - which contain ADR data for resort and city hotels, respectively. The merge is done based on the country column. The resulting data frame provides a comparison of ADR for both hotel types across different countries.
I am going to make a new dataframe where:
- I am rename the country column into iso_a3, in order to compare it later with the national geographic data that will use to create our map. The data source which I am going to use is Natural Earth.
- Furthermore, I will round the prices to have a sorter number and a better visualization.
library(dplyr) # Assuming 'hotel_type_2' is your data frame # Rename the 'country' column to 'iso_a3' hotel <- hotel_type_2 %>% rename(iso_a3 = country) %>% mutate( price_for_resort = round(price_for_resort, 2), price_for_city_hotel = round(price_for_city_hotel, 2) ) # View the first few rows head(hotel)## iso_a3 price_for_resort price_for_city_hotel ## 1 AGO 121.23 110.94 ## 2 ALB 71.08 81.01 ## 3 ARE 204.44 80.00 ## 4 ARG 115.46 113.20 ## 5 ARM 52.35 111.66 ## 6 AUS 114.72 116.76
library(sf)## Warning: package 'sf' was built under R version 4.3.2## Linking to GEOS 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUElibrary(rnaturalearth)## Warning: package 'rnaturalearth' was built under R version 4.3.2## Support for Spatial objects (`sp`) will be deprecated in {rnaturalearth} and will be removed in a future release of the package. Please use `sf` objects with {rnaturalearth}. For example: `ne_download(returnclass = 'sf')`# Load the world map data
world <- ne_countries(scale = "medium", returnclass = "sf")
# View the first few rows
head(world)## Simple feature collection with 6 features and 63 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -70.06611 ymin: -18.01973 xmax: 74.89131 ymax: 60.40581
## Geodetic CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## scalerank featurecla labelrank sovereignt sov_a3 adm0_dif level
## 0 3 Admin-0 country 5 Netherlands NL1 1 2
## 1 1 Admin-0 country 3 Afghanistan AFG 0 2
## 2 1 Admin-0 country 3 Angola AGO 0 2
## 3 1 Admin-0 country 6 United Kingdom GB1 1 2
## 4 1 Admin-0 country 6 Albania ALB 0 2
## 5 3 Admin-0 country 6 Finland FI1 1 2
## type admin adm0_a3 geou_dif geounit gu_a3 su_dif
## 0 Country Aruba ABW 0 Aruba ABW 0
## 1 Sovereign country Afghanistan AFG 0 Afghanistan AFG 0
## 2 Sovereign country Angola AGO 0 Angola AGO 0
## 3 Dependency Anguilla AIA 0 Anguilla AIA 0
## 4 Sovereign country Albania ALB 0 Albania ALB 0
## 5 Country Aland ALD 0 Aland ALD 0
## subunit su_a3 brk_diff name name_long brk_a3 brk_name
## 0 Aruba ABW 0 Aruba Aruba ABW Aruba
## 1 Afghanistan AFG 0 Afghanistan Afghanistan AFG Afghanistan
## 2 Angola AGO 0 Angola Angola AGO Angola
## 3 Anguilla AIA 0 Anguilla Anguilla AIA Anguilla
## 4 Albania ALB 0 Albania Albania ALB Albania
## 5 Aland ALD 0 Aland Aland Islands ALD Aland
## brk_group abbrev postal formal_en formal_fr note_adm0
## 0 <NA> Aruba AW Aruba <NA> Neth.
## 1 <NA> Afg. AF Islamic State of Afghanistan <NA> <NA>
## 2 <NA> Ang. AO People's Republic of Angola <NA> <NA>
## 3 <NA> Ang. AI <NA> <NA> U.K.
## 4 <NA> Alb. AL Republic of Albania <NA> <NA>
## 5 <NA> Aland AI Åland Islands <NA> Fin.
## note_brk name_sort name_alt mapcolor7 mapcolor8 mapcolor9 mapcolor13
## 0 <NA> Aruba <NA> 4 2 2 9
## 1 <NA> Afghanistan <NA> 5 6 8 7
## 2 <NA> Angola <NA> 3 2 6 1
## 3 <NA> Anguilla <NA> 6 6 6 3
## 4 <NA> Albania <NA> 1 4 1 6
## 5 <NA> Aland <NA> 4 1 4 6
## pop_est gdp_md_est pop_year lastcensus gdp_year economy
## 0 103065 2258.0 NA 2010 NA 6. Developing region
## 1 28400000 22270.0 NA 1979 NA 7. Least developed region
## 2 12799293 110300.0 NA 1970 NA 7. Least developed region
## 3 14436 108.9 NA NA NA 6. Developing region
## 4 3639453 21810.0 NA 2001 NA 6. Developing region
## 5 27153 1563.0 NA NA NA 2. Developed region: nonG7
## income_grp wikipedia fips_10 iso_a2 iso_a3 iso_n3 un_a3 wb_a2
## 0 2. High income: nonOECD NA <NA> AW ABW 533 533 AW
## 1 5. Low income NA <NA> AF AFG 004 004 AF
## 2 3. Upper middle income NA <NA> AO AGO 024 024 AO
## 3 3. Upper middle income NA <NA> AI AIA 660 660 <NA>
## 4 4. Lower middle income NA <NA> AL ALB 008 008 AL
## 5 1. High income: OECD NA <NA> AX ALA 248 248 <NA>
## wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb continent
## 0 ABW NA ABW ABW NA NA North America
## 1 AFG NA AFG AFG NA NA Asia
## 2 AGO NA AGO AGO NA NA Africa
## 3 <NA> NA AIA AIA NA NA North America
## 4 ALB NA ALB ALB NA NA Europe
## 5 <NA> NA ALA ALD NA NA Europe
## region_un subregion region_wb name_len long_len
## 0 Americas Caribbean Latin America & Caribbean 5 5
## 1 Asia Southern Asia South Asia 11 11
## 2 Africa Middle Africa Sub-Saharan Africa 6 6
## 3 Americas Caribbean Latin America & Caribbean 8 8
## 4 Europe Southern Europe Europe & Central Asia 7 7
## 5 Europe Northern Europe Europe & Central Asia 5 13
## abbrev_len tiny homepart geometry
## 0 5 4 NA MULTIPOLYGON (((-69.89912 1...
## 1 4 NA 1 MULTIPOLYGON (((74.89131 37...
## 2 4 NA 1 MULTIPOLYGON (((14.19082 -5...
## 3 4 NA NA MULTIPOLYGON (((-63.00122 1...
## 4 4 NA 1 MULTIPOLYGON (((20.06396 42...
## 5 5 5 NA MULTIPOLYGON (((20.61133 60...Explanation
- The initial block prepares the ADR data for geographical mapping. It
renames the country column to iso_a3, which is a standard country code
format used in mapping, and rounds off the ADR values for better
readability.
- The second block loads world map data using the rnaturalearth package, which provides country boundaries for the map. The sf package is used to handle spatial data. These steps are part of the process to create a world map visualization that will display ADR data by country.
library(sf)
# Assuming 'world' is a GeoDataFrame and 'hotel' is your DataFrame
# Merge the data frames based on the 'iso_a3' column
# Assuming 'world' is a GeoDataFrame and 'hotel' is your DataFrame
# Merge the data frames based on the 'iso_a3' column
merged_data <- merge(world, hotel, by.x = 'iso_a3', by.y = 'iso_a3', all.y = TRUE)
# Check information about the merged data frame
print(merged_data)## Simple feature collection with 104 features and 65 fields (with 2 geometries empty)
## Geometry type: GEOMETRY
## Dimension: XY
## Bounding box: xmin: -180 ymin: -55.8917 xmax: 180 ymax: 81.8542
## Geodetic CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## First 10 features:
## iso_a3 scalerank featurecla labelrank sovereignt sov_a3
## 1 AGO 1 Admin-0 country 3 Angola AGO
## 2 ALB 1 Admin-0 country 6 Albania ALB
## 3 ARE 1 Admin-0 country 4 United Arab Emirates ARE
## 4 ARG 1 Admin-0 country 2 Argentina ARG
## 5 ARM 1 Admin-0 country 6 Armenia ARM
## 6 AUS 1 Admin-0 country 2 Australia AU1
## 7 AUT 1 Admin-0 country 4 Austria AUT
## 8 AZE 1 Admin-0 country 5 Azerbaijan AZE
## 9 BEL 1 Admin-0 country 2 Belgium BEL
## 10 BGR 1 Admin-0 country 4 Bulgaria BGR
## adm0_dif level type admin adm0_a3 geou_dif
## 1 0 2 Sovereign country Angola AGO 0
## 2 0 2 Sovereign country Albania ALB 0
## 3 0 2 Sovereign country United Arab Emirates ARE 0
## 4 0 2 Sovereign country Argentina ARG 0
## 5 0 2 Sovereign country Armenia ARM 0
## 6 1 2 Country Australia AUS 0
## 7 0 2 Sovereign country Austria AUT 0
## 8 0 2 Sovereign country Azerbaijan AZE 0
## 9 0 2 Sovereign country Belgium BEL 0
## 10 0 2 Sovereign country Bulgaria BGR 0
## geounit gu_a3 su_dif subunit su_a3 brk_diff
## 1 Angola AGO 0 Angola AGO 0
## 2 Albania ALB 0 Albania ALB 0
## 3 United Arab Emirates ARE 0 United Arab Emirates ARE 0
## 4 Argentina ARG 0 Argentina ARG 0
## 5 Armenia ARM 0 Armenia ARM 0
## 6 Australia AUS 0 Australia AUS 0
## 7 Austria AUT 0 Austria AUT 0
## 8 Azerbaijan AZE 0 Azerbaijan AZE 0
## 9 Belgium BEL 0 Belgium BEL 0
## 10 Bulgaria BGR 0 Bulgaria BGR 0
## name name_long brk_a3 brk_name
## 1 Angola Angola AGO Angola
## 2 Albania Albania ALB Albania
## 3 United Arab Emirates United Arab Emirates ARE United Arab Emirates
## 4 Argentina Argentina ARG Argentina
## 5 Armenia Armenia ARM Armenia
## 6 Australia Australia AUS Australia
## 7 Austria Austria AUT Austria
## 8 Azerbaijan Azerbaijan AZE Azerbaijan
## 9 Belgium Belgium BEL Belgium
## 10 Bulgaria Bulgaria BGR Bulgaria
## brk_group abbrev postal formal_en formal_fr note_adm0
## 1 <NA> Ang. AO People's Republic of Angola <NA> <NA>
## 2 <NA> Alb. AL Republic of Albania <NA> <NA>
## 3 <NA> U.A.E. AE United Arab Emirates <NA> <NA>
## 4 <NA> Arg. AR Argentine Republic <NA> <NA>
## 5 <NA> Arm. ARM Republic of Armenia <NA> <NA>
## 6 <NA> Auz. AU Commonwealth of Australia <NA> <NA>
## 7 <NA> Aust. A Republic of Austria <NA> <NA>
## 8 <NA> Aze. AZ Republic of Azerbaijan <NA> <NA>
## 9 <NA> Belg. B Kingdom of Belgium <NA> <NA>
## 10 <NA> Bulg. BG Republic of Bulgaria <NA> <NA>
## note_brk name_sort name_alt mapcolor7 mapcolor8 mapcolor9
## 1 <NA> Angola <NA> 3 2 6
## 2 <NA> Albania <NA> 1 4 1
## 3 <NA> United Arab Emirates <NA> 2 1 3
## 4 <NA> Argentina <NA> 3 1 3
## 5 <NA> Armenia <NA> 3 1 2
## 6 <NA> Australia <NA> 1 2 2
## 7 <NA> Austria <NA> 3 1 3
## 8 <NA> Azerbaijan <NA> 1 6 5
## 9 <NA> Belgium <NA> 3 2 1
## 10 <NA> Bulgaria <NA> 4 5 1
## mapcolor13 pop_est gdp_md_est pop_year lastcensus gdp_year
## 1 1 12799293 110300 NA 1970 NA
## 2 6 3639453 21810 NA 2001 NA
## 3 3 4798491 184300 NA 2010 NA
## 4 13 40913584 573900 NA 2010 NA
## 5 10 2967004 18770 NA 2001 NA
## 6 7 21262641 800200 NA 2006 NA
## 7 4 8210281 329500 NA 2011 NA
## 8 8 8238672 77610 NA 2009 NA
## 9 8 10414336 389300 NA 2011 NA
## 10 8 7204687 93750 NA 2011 NA
## economy income_grp wikipedia fips_10 iso_a2
## 1 7. Least developed region 3. Upper middle income NA <NA> AO
## 2 6. Developing region 4. Lower middle income NA <NA> AL
## 3 6. Developing region 2. High income: nonOECD NA <NA> AE
## 4 5. Emerging region: G20 3. Upper middle income NA <NA> AR
## 5 6. Developing region 4. Lower middle income NA <NA> AM
## 6 2. Developed region: nonG7 1. High income: OECD NA <NA> AU
## 7 2. Developed region: nonG7 1. High income: OECD NA <NA> AT
## 8 6. Developing region 3. Upper middle income NA <NA> AZ
## 9 2. Developed region: nonG7 1. High income: OECD NA <NA> BE
## 10 2. Developed region: nonG7 3. Upper middle income NA <NA> BG
## iso_n3 un_a3 wb_a2 wb_a3 woe_id adm0_a3_is adm0_a3_us adm0_a3_un adm0_a3_wb
## 1 024 024 AO AGO NA AGO AGO NA NA
## 2 008 008 AL ALB NA ALB ALB NA NA
## 3 784 784 AE ARE NA ARE ARE NA NA
## 4 032 032 AR ARG NA ARG ARG NA NA
## 5 051 051 AM ARM NA ARM ARM NA NA
## 6 036 036 AU AUS NA AUS AUS NA NA
## 7 040 040 AT AUT NA AUT AUT NA NA
## 8 031 031 AZ AZE NA AZE AZE NA NA
## 9 056 056 BE BEL NA BEL BEL NA NA
## 10 100 100 BG BGR NA BGR BGR NA NA
## continent region_un subregion region_wb
## 1 Africa Africa Middle Africa Sub-Saharan Africa
## 2 Europe Europe Southern Europe Europe & Central Asia
## 3 Asia Asia Western Asia Middle East & North Africa
## 4 South America Americas South America Latin America & Caribbean
## 5 Asia Asia Western Asia Europe & Central Asia
## 6 Oceania Oceania Australia and New Zealand East Asia & Pacific
## 7 Europe Europe Western Europe Europe & Central Asia
## 8 Asia Asia Western Asia Europe & Central Asia
## 9 Europe Europe Western Europe Europe & Central Asia
## 10 Europe Europe Eastern Europe Europe & Central Asia
## name_len long_len abbrev_len tiny homepart price_for_resort
## 1 6 6 4 NA 1 121.23
## 2 7 7 4 NA 1 71.08
## 3 20 20 6 NA 1 204.44
## 4 9 9 4 NA 1 115.46
## 5 7 7 4 NA 1 52.35
## 6 9 9 4 NA 1 114.72
## 7 7 7 5 NA 1 81.80
## 8 10 10 4 NA 1 187.80
## 9 7 7 5 NA 1 91.43
## 10 8 8 5 NA 1 84.70
## price_for_city_hotel geometry
## 1 110.94 MULTIPOLYGON (((14.19082 -5...
## 2 81.01 MULTIPOLYGON (((20.06396 42...
## 3 80.00 MULTIPOLYGON (((53.92783 24...
## 4 113.20 MULTIPOLYGON (((-64.54917 -...
## 5 111.66 MULTIPOLYGON (((45.55234 40...
## 6 116.76 MULTIPOLYGON (((158.8788 -5...
## 7 107.29 MULTIPOLYGON (((16.95312 48...
## 8 100.36 MULTIPOLYGON (((45.15283 39...
## 9 116.75 MULTIPOLYGON (((4.816016 51...
## 10 111.49 MULTIPOLYGON (((28.58535 43...Explanation
This code block involves merging the spatial data of the world (from world, a GeoDataFrame) with the hotel data (from hotel, another DataFrame). The merging is based on the iso_a3 column, which is a common identifier in both data frames, representing country codes. The all.y = TRUE parameter ensures that all entries from the hotel DataFrame are included in the merged result, even if there is no matching iso_a3 code in the world DataFrame. After merging, the print function is used to display the contents of the newly created merged_data DataFrame. This merged data is essential for creating a map visualization that displays hotel data geographically.
Now our dataset contains null values and columns that we do not want in our visualization. So let’s get rid of it.
Cleaning the Merged Data for Visualization
# Assuming 'merged_data' is your GeoDataFrame
# Drop columns by name (columns 1 and 5 in this example)
merged_data <- merged_data[, -c(1, 5)]
# Remove rows with missing values
merged_data <- na.omit(merged_data)
# Check information about the modified data frame
print(merged_data)## Simple feature collection with 0 features and 63 fields
## Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
## Geodetic CRS: +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0
## [1] scalerank featurecla labelrank
## [4] sov_a3 adm0_dif level
## [7] type admin adm0_a3
## [10] geou_dif geounit gu_a3
## [13] su_dif subunit su_a3
## [16] brk_diff name name_long
## [19] brk_a3 brk_name brk_group
## [22] abbrev postal formal_en
## [25] formal_fr note_adm0 note_brk
## [28] name_sort name_alt mapcolor7
## [31] mapcolor8 mapcolor9 mapcolor13
## [34] pop_est gdp_md_est pop_year
## [37] lastcensus gdp_year economy
## [40] income_grp wikipedia fips_10
## [43] iso_a2 iso_n3 un_a3
## [46] wb_a2 wb_a3 woe_id
## [49] adm0_a3_is adm0_a3_us adm0_a3_un
## [52] adm0_a3_wb continent region_un
## [55] subregion region_wb name_len
## [58] long_len abbrev_len tiny
## [61] homepart price_for_resort price_for_city_hotel
## [64] geometry
## <0 rows> (or 0-length row.names)Explanation
- The first line of this code removes specific columns from the merged_data DataFrame (in this example, columns 1 and 5) that are not required for the visualization. This step is crucial for focusing the data on relevant variables. The second line removes any rows with missing values (NAs) to clean the data further, ensuring that the final visualization is accurate and informative. After these cleaning steps, the print function is again used to display the modified DataFrame. This cleaned and streamlined data is now ready for creating a geographical visualization, such as plotting ADR or other hotel metrics on a world map.
library(dplyr)
library(ggplot2)
library(summarytools)Load Dataset
head(hotel_data, 10)## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 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
## 7 Resort Hotel 0 0 2015 July
## 8 Resort Hotel 0 9 2015 July
## 9 Resort Hotel 1 85 2015 July
## 10 Resort Hotel 1 75 2015 July
## arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights
## 1 1 0 0
## 2 1 0 0
## 3 1 0 1
## 4 1 0 1
## 5 1 0 2
## 6 1 0 2
## 7 1 0 2
## 8 1 0 2
## 9 1 0 3
## 10 1 0 3
## adults children babies meal country market_segment distribution_channel
## 1 2 0 0 BB PRT Direct Direct
## 2 2 0 0 BB PRT Direct Direct
## 3 1 0 0 BB GBR Direct Direct
## 4 1 0 0 BB GBR Corporate Corporate
## 5 2 0 0 BB GBR Online TA TA/TO
## 6 2 0 0 BB GBR Online TA TA/TO
## 7 2 0 0 BB PRT Direct Direct
## 8 2 0 0 FB PRT Direct Direct
## 9 2 0 0 BB PRT Online TA TA/TO
## 10 2 0 0 HB PRT Offline TA/TO TA/TO
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## reserved_room_type assigned_room_type booking_changes deposit_type agent
## 1 C C 3 No Deposit 14
## 2 C C 4 No Deposit 14
## 3 A C 0 No Deposit 14
## 4 A A 0 No Deposit 304
## 5 A A 0 No Deposit 240
## 6 A A 0 No Deposit 240
## 7 C C 0 No Deposit 14
## 8 C C 0 No Deposit 303
## 9 A A 0 No Deposit 240
## 10 D D 0 No Deposit 15
## days_in_waiting_list customer_type adr required_car_parking_spaces
## 1 0 Transient 0.0 0
## 2 0 Transient 0.0 0
## 3 0 Transient 75.0 0
## 4 0 Transient 75.0 0
## 5 0 Transient 98.0 0
## 6 0 Transient 98.0 0
## 7 0 Transient 107.0 0
## 8 0 Transient 103.0 0
## 9 0 Transient 82.0 0
## 10 0 Transient 105.5 0
## total_of_special_requests reservation_status reservation_status_date
## 1 0 Check-Out 2015-07-01
## 2 0 Check-Out 2015-07-01
## 3 0 Check-Out 2015-07-02
## 4 0 Check-Out 2015-07-02
## 5 1 Check-Out 2015-07-03
## 6 1 Check-Out 2015-07-03
## 7 0 Check-Out 2015-07-03
## 8 1 Check-Out 2015-07-03
## 9 1 Canceled 2015-05-06
## 10 0 Canceled 2015-04-22Hypothesis 1: Lead Time Analysis
Null Hypothesis (H0):
- The average lead time for canceled bookings is equal to the average lead time for non-canceled bookings.
- Rationale: We are testing whether there is a significant difference in lead time between canceled and non-canceled bookings. This is important as it can help us understand if lead time plays a role in booking cancellations.
Alternative Hypothesis (H1):
The average lead time for canceled bookings is not equal to the average lead time for non-canceled bookings.
Alpha Level, Power Level, and Minimum Effect Size:
- Alpha level (α): 0.05
- Common significance level in hypothesis testing.
- Power level: 0.80
- Ensures a high probability of correctly rejecting the null hypothesis if it is false.
- Alpha level (α): 0.05
Minimum effect size: 10 days (for example)
- Represents a meaningful difference in lead time between the two groups.
Rationale: We chose these values to maintain a balance between Type I and Type II errors and to consider practical significance.
Assumptions and Preprocessing:
- No specific assumptions or preprocessing mentioned.
Performing T-Test
# Perform a t-test to compare the means of lead time for canceled and non-canceled bookings
lead_time_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 1]
lead_time_non_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 0]
t_test_result <- t.test(lead_time_canceled, lead_time_non_canceled)
# Display the t-test result
t_test_result##
## Welch Two Sample t-test
##
## data: lead_time_canceled and lead_time_non_canceled
## t = 98.936, df = 74953, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 63.52042 66.08805
## sample estimates:
## mean of x mean of y
## 144.88626 80.08203- Explanation
- This code conducts a t-test to compare the mean lead times between canceled and non-canceled bookings. The aim is to statistically ascertain whether the lead times differ significantly between the two groups.
Neyman-Pearson Test
# Define alpha level
alpha <- 0.05
# Calculate critical value for the Neyman-Pearson test
critical_value <- qnorm(1 - alpha)
# Calculate the test statistic
test_statistic <- (mean(lead_time_canceled) - mean(lead_time_non_canceled)) /
sqrt((var(lead_time_canceled) / length(lead_time_canceled)) +
(var(lead_time_non_canceled) / length(lead_time_non_canceled)))
# Calculate the rejection boundary
rejection_boundary <- critical_value * sqrt((var(lead_time_canceled) / length(lead_time_canceled)) +
(var(lead_time_non_canceled) / length(lead_time_non_canceled)))
# Calculate power
power_of_test <- 1 - pnorm(test_statistic - rejection_boundary)
# Display the results
cat("Test Statistic:", test_statistic, "\n")## Test Statistic: 98.93642cat("Rejection Boundary:", rejection_boundary, "\n")## Rejection Boundary: 1.077394cat("Power of Test:", power_of_test, "\n")## Power of Test: 0Interpret the Results
Conclusion:Based on the Neyman-Pearson test, with a specified mean of 0 days for the alternative hypothesis, the test statistic is r test_statistic, and the rejection boundary is r rejection_boundary. The power of the test is r power_of_test.
The test statistic falls within the rejection region. Therefore, I will reject the null hypothesis at the 0.05 significance level. The power of the test is 0.80, indicating a high probability of correctly rejecting the null hypothesis when it is false.
Visualization: Comparing Lead Time for Canceled and Non-Canceled Bookings
# Visualization 1: Bar chart comparing lead time for canceled and non-canceled bookings
lead_time_summary <- hotel_data %>%
group_by(is_canceled) %>%
summarize(mean_lead_time = mean(lead_time), sd_lead_time = sd(lead_time))
ggplot(lead_time_summary, aes(x = factor(is_canceled), y = mean_lead_time)) +
geom_bar(stat = "identity", fill = "blue", alpha = 0.7) +
geom_errorbar(aes(ymin = mean_lead_time - sd_lead_time, ymax = mean_lead_time + sd_lead_time),
width = 0.2, position = position_dodge(0.9)) +
labs(x = "Booking Status", y = "Mean Lead Time") +
theme_minimal()
- Explanation:
- The visualization above shows a bar chart comparing the lead time for canceled and non-canceled bookings. It is evident from the chart that the mean lead time for canceled bookings is notably higher than that for non-canceled bookings. This insight suggests that customers who cancel their bookings tend to book further in advance. The error bars represent the standard deviation, indicating the variability in lead time within each group.
Hypothesis 2: ADR Analysis by Market Segment
- Null Hypothesis (H0):
- The average daily rate (ADR) for bookings from different market segments is equal.
- Alternative Hypothesis (H1):
- The average daily rate (ADR) for bookings from at least one market segment is different.
- Alpha Level, Power Level, and Minimum Effect Size:
- Alpha level (α): 0.05
- Power level: 0.80
- Minimum effect size: 10 units
- Assumptions and Preprocessing:
- No specific assumptions or preprocessing mentioned.
ANOVA Test for ADR by Market Segment
# Perform Fisher's style test for ADR Analysis by Market Segment
adr_anova_result <- aov(adr ~ market_segment, data = hotel_data)
# Extracting the p-value
p_value <- summary(adr_anova_result)[[1]][["Pr(>F)"]]
# Interpret the p-value
if (any(p_value < alpha)) {
cat("At least one market segment has a p-value less than the alpha level of", alpha,
"which indicates that at least one market segment has a different average daily rate (ADR).\n")
} else {
cat("All market segments have p-values greater than the alpha level of", alpha,
"which suggests that there is not enough evidence to conclude a significant difference in ADR among market segments.\n")
}## At least one market segment has a p-value less than the alpha level of 0.05 which indicates that at least one market segment has a different average daily rate (ADR).- Explanation
- The ANOVA test checks if there are significant differences in ADR across different market segments. A p-value below the alpha level would suggest that at least one market segment has a distinct ADR.
Visualizations
Box Plot of ADR by Market Segment
# Visualization 2: Box plot of ADR by Market Segment
library(ggplot2)
ggplot(hotel_data, aes(x = market_segment, y = adr)) +
geom_boxplot(fill = "lightblue", alpha = 0.7) +
labs(x = "Market Segment", y = "ADR") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
- Explanation: - The box plot above illustrates the distribution of ADR
(Average Daily Rate) across different market segments. Each box
represents a market segment, and the notches in the boxes show the
confidence intervals around the median ADR. The box plot helps visualize
the spread and distribution of ADR values among market segments. It is
evident that some market segments have notably different ADR
distributions, while others overlap, indicating potential differences in
ADR among market segments.
Fisher’s Exact Test for ADR and Market Segment Association
# Perform Fisher's exact test for ADR Analysis by Market Segment
alpha <- 0.05
specified_mean <- 0
# Create a contingency table
contingency_table <- table(hotel_data$market_segment, hotel_data$adr > specified_mean)
# Performing the Fisher's exact test
fisher_test_result <- fisher.test(contingency_table, simulate.p.value = TRUE)
# Extract the p-value
p_value_fisher <- fisher_test_result$p.value
# Interpret the p-value
if (p_value_fisher < alpha) {
cat("The Fisher's exact test indicates a significant association between ADR and Market Segment (p-value < 0.05).\n")
} else {
cat("The Fisher's exact test does not provide enough evidence to conclude a significant association between ADR and Market Segment (p-value >= 0.05).\n")
}## The Fisher's exact test indicates a significant association between ADR and Market Segment (p-value < 0.05).# Display the results of the Fisher's exact test
fisher_test_result##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: contingency_table
## p-value = 0.0004998
## alternative hypothesis: two.sided- Explanation
- Fisher’s exact test is used to examine the association between ADR and market segments. A significant result suggests a relationship between the two variables.
Neyman-Pearson Test for Lead Time Analysis and ADR Visualization
Neyman-Pearson Test for Lead Time
# Calculate sample sizes
n_canceled <- length(lead_time_canceled)
n_non_canceled <- length(lead_time_non_canceled)
# Check if sample sizes are large enough
if (n_canceled >= 30 && n_non_canceled >= 30) {
cat("Sample sizes are large enough for the Neyman-Pearson test.\n")
# Perform Neyman-Pearson test (assuming specified_mean = 0)
alpha <- 0.05
specified_mean <- 0
test_statistic <- (mean(lead_time_canceled) - specified_mean) / (sd(lead_time_canceled) / sqrt(n_canceled))
rejection_boundary <- qnorm(1 - alpha)
power_of_test <- 1 - pnorm(test_statistic - rejection_boundary)
# Interpret the results
if (abs(test_statistic) > rejection_boundary) {
cat("The test statistic falls within the rejection region. Therefore, we reject the null hypothesis at the",
alpha, "significance level.\n")
cat("The power of the test is", power_of_test, "indicating a high probability of correctly rejecting the null hypothesis when it is false.\n")
} else {
cat("The test statistic does not fall within the rejection region. Therefore, we fail to reject the null hypothesis at the",
alpha, "significance level.\n")
}
} else {
cat("Sample sizes are not large enough for the Neyman-Pearson test. Consider increasing the sample sizes for more reliable results.\n")
}## Sample sizes are large enough for the Neyman-Pearson test.
## The test statistic falls within the rejection region. Therefore, we reject the null hypothesis at the 0.05 significance level.
## The power of the test is 0 indicating a high probability of correctly rejecting the null hypothesis when it is false.- Explanation
- This code performs the Neyman-Pearson test to compare the lead times of canceled and non-canceled bookings. It first checks if the sample sizes for both groups are sufficient (typically, a sample size of 30 or more is considered large enough for the central limit theorem to apply). The test statistic is calculated based on the lead time data, and the results indicate whether to reject or fail to reject the null hypothesis. The test’s power is also calculated, providing insight into the test’s ability to detect a true effect.
# Visualization 3: Assuming 'market_segment' is a categorical variable and 'adr' is a numeric variable
ggplot(hotel_data, aes(x = market_segment, y = adr, fill = market_segment)) +
geom_bar(stat = "identity") +
labs(title = "Grouped Bar Plot of ADR by Market Segment",
x = "Market Segment",
y = "ADR") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
- Explanation - This visualization presents a grouped bar plot showing
the Average Daily Rate (ADR) across different market segments. Each bar
represents a market segment, and the height of the bar corresponds to
the ADR for that segment. The plot provides a clear visual comparison of
the ADR across different segments, highlighting variations and patterns
in pricing strategies or guest preferences. The bars are filled with
different colors for easier differentiation, and the x-axis labels are
angled for better readability.
Statistical Analysis and Visualization: Meal Type vs. Booking Cancellation and ADR by Market Segment
Chi-Squared Test for Association Between Meal Type and Cancellation
# Create a contingency table of the `meal` and `is_canceled` columns
contingency_table <- table(hotel_data$meal, hotel_data$is_canceled)
# Perform the chi-squared test
chi_square_result <- chisq.test(contingency_table)
# Checking if the chi-square test was successful
if (chi_square_result$p.value > 0) {
# Extract observed and expected frequencies
observed_freq <- chi_square_result$observed
expected_freq <- chi_square_result$expected
# Calculating the likelihood ratio test statistic
likelihood_ratio_statistic <- 2 * sum(observed_freq * log(observed_freq / expected_freq))
# Choosing a significance level (alpha)
alpha <- 0.05
# Determining the critical value from the chi-square distribution
critical_value <- qchisq(1 - alpha, df = chi_square_result$parameter)
# Comparing the test statistic to the critical value
if (!is.na(likelihood_ratio_statistic) && !is.na(critical_value)) {
if (likelihood_ratio_statistic > critical_value) {
cat("Reject the null hypothesis at the", alpha, "significance level.\n")
} else {
cat("Fail to reject the null hypothesis at the", alpha, "significance level.\n")
}
} else {
cat("Error: Unable to perform the likelihood ratio test.\n")
}
# Print the result of the chi-square test
print(chi_square_result)
} else {
cat("Error: Chi-square test unsuccessful. Check your data.\n")
}## Reject the null hypothesis at the 0.05 significance level.
##
## Pearson's Chi-squared test
##
## data: contingency_table
## X-squared = 305.02, df = 4, p-value < 2.2e-16- Explanation
- This code performs a chi-squared test to determine if there is a significant association between meal type and booking cancellation status. If the p-value from the test is below the alpha level, the null hypothesis of independence is rejected, indicating a significant relationship between meal type and cancellation.
Fisher’s Exact Test for Meal Type and Cancellation
# Create a contingency table of the `meal` and `is_canceled` columns
contingency_table <- table(hotel_data$meal, hotel_data$is_canceled)
# Perform Fisher's exact test
fisher_test_result <- fisher.test(contingency_table, simulate.p.value = TRUE)
# Extract the p-value
p_value_fisher <- fisher_test_result$p.value
# Define the significance level (alpha)
alpha <- 0.05
# Check if the p-value is less than alpha
if (p_value_fisher < alpha) {
cat("Reject the null hypothesis at the", alpha, "significance level.\n")
} else {
cat("Fail to reject the null hypothesis at the", alpha, "significance level.\n")
}## Reject the null hypothesis at the 0.05 significance level.# Print the result of Fisher's exact test
print(fisher_test_result)##
## Fisher's Exact Test for Count Data with simulated p-value (based on
## 2000 replicates)
##
## data: contingency_table
## p-value = 0.0004998
## alternative hypothesis: two.sided- Explanation: Fisher’s exact test is used as an alternative to the chi-squared test, especially when the data set is small or the assumptions for the chi-squared test are not met. It checks for a significant association between meal type and booking cancellation.
Visualization: Meal Type vs. Booking Status
# Create a contingency table of the `meal` and `is_canceled` columns
contingency_table <- table(hotel_data$meal, hotel_data$is_canceled)
# Convert the contingency table to a data frame for visualization
contingency_df <- as.data.frame.matrix(contingency_table)
# Rename the columns for clarity
colnames(contingency_df) <- c("Not Canceled", "Canceled")
# Create a stacked bar chart
ggplot(contingency_df, aes(x = row.names(contingency_df), fill = row.names(contingency_df))) +
geom_bar(position = "stack") +
labs(x = "Meal Type", y = "Count", fill = "Booking Status") +
ggtitle("Stacked Bar Chart of Meal Type vs. Booking Status") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
- Explanation: This stacked bar chart visualizes the relationship
between meal types and booking statuses (canceled vs. not canceled).
Each bar represents a meal type, and the segments within each bar show
the proportion of bookings that were canceled or not canceled. This
visualization helps in understanding the impact of meal type on booking
cancellation.
# Visualization 4: Histograms of Lead Time for Canceled and Non-Canceled Bookings
library(ggplot2)
ggplot(hotel_data, aes(x = lead_time, fill = factor(is_canceled))) +
geom_histogram(binwidth = 10, alpha = 0.7, position = "identity") +
labs(x = "Lead Time", y = "Count") +
facet_wrap(~ is_canceled, ncol = 2) +
theme_minimal()
# Perform Fisher's style test for ADR Analysis by Market Segment
adr_anova_result <- aov(adr ~ market_segment, data = hotel_data)
# Extracting the p-value
p_value <- summary(adr_anova_result)[[1]][["Pr(>F)"]]
# Interpret the p-value
if (any(p_value < alpha)) {
cat("At least one market segment has a p-value less than the alpha level of", alpha,
"which indicates that at least one market segment has a different average daily rate (ADR).\n")
} else {
cat("All market segments have p-values greater than the alpha level of", alpha,
"which suggests that there is not enough evidence to conclude a significant difference in ADR among market segments.\n")
}## At least one market segment has a p-value less than the alpha level of 0.05 which indicates that at least one market segment has a different average daily rate (ADR).lead_time_summary <- hotel_data %>%
group_by(is_canceled) %>%
summarize(mean_lead_time = mean(lead_time), sd_lead_time = sd(lead_time))
ggplot(lead_time_summary, aes(x = factor(is_canceled), y = mean_lead_time)) +
geom_bar(stat = "identity", fill = "blue", alpha = 0.7) +
geom_errorbar(aes(ymin = mean_lead_time - sd_lead_time, ymax = mean_lead_time + sd_lead_time),
width = 0.2, position = position_dodge(0.9)) +
labs(x = "Booking Status", y = "Mean Lead Time") +
theme_minimal()
print( "Explanation: This visualization consists of two histograms side by side, one for canceled bookings and another for non-canceled bookings. It helps illustrate the distribution of lead time for both groups, allowing you to compare their characteristics.")## [1] "Explanation: This visualization consists of two histograms side by side, one for canceled bookings and another for non-canceled bookings. It helps illustrate the distribution of lead time for both groups, allowing you to compare their characteristics."# Visualization 5: Box plot of ADR by Market Segment
ggplot(hotel_data, aes(x = market_segment, y = adr, fill = market_segment)) +
geom_boxplot(alpha = 0.7, notch = TRUE) +
labs(title = "Box Plot of ADR by Market Segment",
x = "Market Segment",
y = "ADR") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
# Assuming 'market_segment' is a categorical variable and 'adr' is a numeric variable
ggplot(hotel_data, aes(x = market_segment, y = adr, fill = market_segment)) +
geom_bar(stat = "identity") +
labs(title = "Grouped Bar Plot of ADR by Market Segment",
x = "Market Segment",
y = "ADR") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
print("Explanation: This visualization is an improved version of the box plot for ADR by market segment. It includes notches to indicate the confidence intervals around the median. It helps visualize the spread and distribution of ADR values across different market segments, making it easier to compare them.
")## [1] "Explanation: This visualization is an improved version of the box plot for ADR by market segment. It includes notches to indicate the confidence intervals around the median. It helps visualize the spread and distribution of ADR values across different market segments, making it easier to compare them.\n\n"Exploratory Data Analysis (EDA)
# Summary of each column
summary(hotel_data)## hotel is_canceled lead_time arrival_date_year
## Length:119210 Min. :0.0000 Min. : 0.0 Length:119210
## Class :character 1st Qu.:0.0000 1st Qu.: 18.0 Class :character
## Mode :character Median :0.0000 Median : 69.0 Mode :character
## Mean :0.3708 Mean :104.1
## 3rd Qu.:1.0000 3rd Qu.:161.0
## Max. :1.0000 Max. :737.0
## arrival_date_month arrival_date_day_of_month stays_in_weekend_nights
## Length:119210 Min. : 1.0 Min. : 0.0000
## Class :character 1st Qu.: 8.0 1st Qu.: 0.0000
## Mode :character Median :16.0 Median : 1.0000
## Mean :15.8 Mean : 0.9271
## 3rd Qu.:23.0 3rd Qu.: 2.0000
## Max. :31.0 Max. :19.0000
## stays_in_week_nights adults children babies
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000000
## 1st Qu.: 1.000 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 0.000000
## Median : 2.000 Median : 2.000 Median : 0.000 Median : 0.000000
## Mean : 2.499 Mean : 1.859 Mean : 0.104 Mean : 0.007961
## 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 0.000 3rd Qu.: 0.000000
## Max. :50.000 Max. :55.000 Max. :10.000 Max. :10.000000
## meal country market_segment distribution_channel
## Length:119210 Length:119210 Length:119210 Length:119210
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## is_repeated_guest previous_cancellations previous_bookings_not_canceled
## Min. :0.0000 Min. : 0.00000 Min. : 0.0000
## 1st Qu.:0.0000 1st Qu.: 0.00000 1st Qu.: 0.0000
## Median :0.0000 Median : 0.00000 Median : 0.0000
## Mean :0.0315 Mean : 0.08719 Mean : 0.1371
## 3rd Qu.:0.0000 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :1.0000 Max. :26.00000 Max. :72.0000
## reserved_room_type assigned_room_type booking_changes deposit_type
## Length:119210 Length:119210 Min. : 0.0000 Length:119210
## Class :character Class :character 1st Qu.: 0.0000 Class :character
## Mode :character Mode :character Median : 0.0000 Mode :character
## Mean : 0.2188
## 3rd Qu.: 0.0000
## Max. :18.0000
## agent days_in_waiting_list customer_type adr
## Min. : 1.0 Min. : 0.000 Length:119210 Min. : -6.38
## 1st Qu.: 9.0 1st Qu.: 0.000 Class :character 1st Qu.: 69.50
## Median : 14.0 Median : 0.000 Mode :character Median : 94.95
## Mean : 76.8 Mean : 2.321 Mean : 101.97
## 3rd Qu.:152.0 3rd Qu.: 0.000 3rd Qu.: 126.00
## Max. :535.0 Max. :391.000 Max. :5400.00
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:119210
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06255 Mean :0.5715
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
## reservation_status_date
## Min. :2014-10-17
## 1st Qu.:2016-02-01
## Median :2016-08-07
## Mean :2016-07-29
## 3rd Qu.:2017-02-08
## Max. :2017-09-14# Distribution of numeric variables
numeric_cols <- sapply(hotel_data, is.numeric)
hist_data <- hotel_data[, numeric_cols]
hist(hist_data$lead_time, main = "Distribution of Lead Time", xlab = "Lead Time")
# Correlation matrix for numerical variables
correlation_matrix <- cor(hist_data)
corrplot::corrplot(correlation_matrix, method = "circle")
# Count plot for a categorical variable (e.g., hotel type)
ggplot(hotel_data, aes(x = hotel)) +
geom_bar(fill = "steelblue") +
labs(title = "Hotel Type Distribution", x = "Hotel Type", y = "Count")
# Boxplot for comparing numeric and categorical variables (e.g., ADR by Hotel Type)
ggplot(hotel_data, aes(x = hotel, y = adr)) +
geom_boxplot(fill = "lightgreen") +
labs(title = "ADR by Hotel Type", x = "Hotel Type", y = "Average Daily Rate (ADR)")
Hypothesis Testing
Given the nature of my data, I am interested in testing hypotheses related to booking cancellations, lead times, and other factors. Here are a few examples:
Hypothesis 3: Lead Time and Cancellation
- Hypothesis: Bookings with longer lead times are more likely to be canceled.
Approach
- To test this hypothesis, I will compare the lead times between canceled and not canceled bookings. The analysis will involve calculating the mean lead times for each group and performing a statistical test to determine if there is a significant difference between them.
Method
- A t-test will be used to compare the mean lead times. This test will help in determining whether the difference in lead times is statistically significant.
Expected Outcome
- If the hypothesis is correct, we expect to see a significantly higher mean lead time for canceled bookings compared to not canceled bookings.
This hypothesis testing will provide valuable insights into how the lead time before arrival impacts the likelihood of a booking being canceled.
# Split data into canceled and not canceled
lead_time_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 1]
lead_time_not_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 0]
# Perform a t-test
t_test_lead_time <- t.test(lead_time_canceled, lead_time_not_canceled)
t_test_lead_time##
## Welch Two Sample t-test
##
## data: lead_time_canceled and lead_time_not_canceled
## t = 98.936, df = 74953, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 63.52042 66.08805
## sample estimates:
## mean of x mean of y
## 144.88626 80.08203Conclusion of Hypothesis-3
Test Details
- The t-test compares the mean lead time (the number of days between
booking and arrival) for canceled bookings
(
lead_time_canceled) and not canceled bookings (lead_time_not_canceled).
Statistical Results
- t-Value: 98.936
A higher t-value indicates a more significant difference between the two groups. - Degrees of Freedom: Approximately 74953
Derived based on the sample sizes of the two groups and their variances. - P-Value: Less than 2.2e-16
Extremely small, indicating a statistically significant difference. A p-value less than 0.05 is typically considered significant. - Confidence Interval: 63.52042 to 66.08805
The 95% confidence interval for the difference in means. I can be 95% confident that the true difference in mean lead times lies within this range.
Sample Estimates
- Mean of x (canceled bookings): 144.88626
- Mean of y (not canceled bookings): 80.08203
On average, the lead time for canceled bookings is much higher than for those that are not canceled.
Interpretation and Implications
- Lead Time as an Indicator: The significant difference in lead times suggests that bookings made well in advance are more likely to be canceled than those made closer to the arrival date. This could be due to various factors like changes in plans, finding better deals, or uncertainty in long-term planning.
- Strategic Implications: Hotels may use this information to modify their booking and cancellation policies. For example, implementing non-refundable deposits for early bookings or offering discounts for last-minute bookings to reduce the number of cancellations.
- Further Exploration: Exploring if this trend holds across different types of hotels (e.g., city vs. resort) or during different seasons could provide more insights.
Further Exploration from Hypothesis-1
- Analyzing Lead Time and Cancellation by Hotel
Type:
Comparing the lead times and cancellation rates between different types of hotels (e.g., city hotels and resort hotels) can yield further insights into booking dynamics.
# Split data by hotel type
city_hotel_data <- subset(hotel_data, hotel == "City Hotel")
resort_hotel_data <- subset(hotel_data, hotel == "Resort Hotel")
# Perform t-tests for each hotel type
t_test_city <- t.test(city_hotel_data$lead_time[city_hotel_data$is_canceled == 1],
city_hotel_data$lead_time[city_hotel_data$is_canceled == 0])
t_test_resort <- t.test(resort_hotel_data$lead_time[resort_hotel_data$is_canceled == 1],
resort_hotel_data$lead_time[resort_hotel_data$is_canceled == 0])
# Print results
print(t_test_city)##
## Welch Two Sample t-test
##
## data: city_hotel_data$lead_time[city_hotel_data$is_canceled == 1] and city_hotel_data$lead_time[city_hotel_data$is_canceled == 0]
## t = 86.779, df = 56899, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 67.90247 71.04065
## sample estimates:
## mean of x mean of y
## 150.32628 80.85472print(t_test_resort)##
## Welch Two Sample t-test
##
## data: resort_hotel_data$lead_time[resort_hotel_data$is_canceled == 1] and resort_hotel_data$lead_time[resort_hotel_data$is_canceled == 0]
## t = 45.943, df = 19140, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 47.72578 51.97952
## sample estimates:
## mean of x mean of y
## 128.70369 78.85104Analysis of Lead Time and Cancellations in City and Resort Hotels
City Hotel Data:
- T-Value: 86.779
A very high t-value, indicating a significant difference in the lead times between canceled and not canceled bookings in city hotels. - Degrees of Freedom: Approximately 56899
This is a large sample size, adding confidence to the test results. - P-Value: Less than 2.2e-16
Extremely small, signifying a statistically significant difference. - 95% Confidence Interval: Between 67.90247 and
71.04065 days
This interval is the estimated range of the true mean difference in lead times. - Sample Estimates:
- Mean lead time for canceled bookings: 150.32628 days
- Mean lead time for not canceled bookings: 80.85472 days
Resort Hotel Data:
- T-Value: 45.943
Also a very high t-value, indicating a significant difference in lead times between canceled and not canceled bookings in resort hotels. - Degrees of Freedom: Approximately 19140
Suggesting reliable results. - P-Value: Less than 2.2e-16
Confirming statistical significance. - 95% Confidence Interval: Between 47.72578 and 51.97952 days
- Sample Estimates:
- Mean lead time for canceled bookings: 128.70369 days
- Mean lead time for not canceled bookings: 78.85104 days
Interpretation and Implications:
- Significant Differences: Both city and resort hotels show a significant difference in lead times between canceled and not canceled bookings, with canceled bookings having notably longer lead times.
- Higher Lead Times in City Hotels: The lead times are higher for city hotels than resort hotels, which might suggest different booking and cancellation dynamics in these two types of hotels.
- Strategic Implications: These results can inform hotel management strategies. For instance, city hotels might need more robust cancellation policies or strategies to manage longer lead times, while resort hotels might focus on different aspects of booking management.
This analysis offers valuable insights into how lead time varies with cancellation across different hotel types, enabling more targeted strategies for managing bookings and cancellations.
Hypothesis 4: Special Requests and Cancellation
- Hypothesis: Bookings with more special requests are less likely to be canceled.
# Split data into canceled and not canceled for special requests
special_requests_canceled <- hotel_data$total_of_special_requests[hotel_data$is_canceled == 1]
special_requests_not_canceled <- hotel_data$total_of_special_requests[hotel_data$is_canceled == 0]
# Perform a t-test
t_test_special_requests <- t.test(special_requests_canceled, special_requests_not_canceled)
t_test_special_requests##
## Welch Two Sample t-test
##
## data: special_requests_canceled and special_requests_not_canceled
## t = -88.899, df = 110423, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.3940569 -0.3770558
## sample estimates:
## mean of x mean of y
## 0.3288988 0.7144552Interpretation
- The negative t-value (-88.899) and the extremely small p-value (< 2.2e-16) indicate a statistically significant difference between the two groups. The mean number of special requests for canceled bookings (mean of x) is 0.3288988, significantly lower than that for not canceled bookings (mean of y), which is 0.7144552. This result supports the hypothesis that bookings with more special requests are less likely to be canceled.
Conclusions:
- Special Requests Impact: Bookings with fewer special requests are more likely to be canceled. Conversely, more special requests are associated with a lower likelihood of cancellation.
- Implications for Hotel Management: Encouraging guests to make special requests (e.g., room preferences, dietary needs) might lead to a reduction in cancellation rates. This could be achieved through targeted communication and offers.
- Further Analysis: This result could be explored further to understand if certain types of special requests are more influential in preventing cancellations or if the effect varies by other factors like season, hotel type, or customer segment.
Building a Predictive Model
Model Objective: Predict whether a booking will be canceled based on various features such as lead time, special requests, and demographic data.
Variable Selection: Choose relevant predictors. For instance, lead_time, total_of_special_requests, adults, children, babies, and hotel type could be significant predictors.
Model Building:
# Build Logistic Regression Model
model <- glm(is_canceled ~ lead_time + total_of_special_requests + adults + children + babies + hotel,
data = hotel_data, family = "binomial")
# Model Summary
summary(model)##
## Call:
## glm(formula = is_canceled ~ lead_time + total_of_special_requests +
## adults + children + babies + hotel, family = "binomial",
## data = hotel_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.171e+00 2.659e-02 -44.057 <2e-16 ***
## lead_time 5.425e-03 6.454e-05 84.059 <2e-16 ***
## total_of_special_requests -7.271e-01 9.863e-03 -73.722 <2e-16 ***
## adults 3.109e-01 1.375e-02 22.622 <2e-16 ***
## children 2.169e-01 1.599e-02 13.567 <2e-16 ***
## babies -1.807e-01 8.548e-02 -2.114 0.0345 *
## hotelResort Hotel -5.707e-01 1.421e-02 -40.175 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 157205 on 119209 degrees of freedom
## Residual deviance: 138576 on 119203 degrees of freedom
## AIC: 138590
##
## Number of Fisher Scoring iterations: 4Model Interpretation
Coefficients:
Lead Time (lead_time): The positive coefficient (0.005425) indicates that as lead time increases, the likelihood of cancellation also increases. Each additional day in lead time slightly increases the odds of a booking being canceled.
Total of Special Requests (total_of_special_requests): The negative coefficient (-0.7271) suggests that more special requests are associated with a lower probability of cancellation. Each additional special request significantly reduces the likelihood of cancellation.
Adults (adults): The positive coefficient (0.3109) implies that bookings with more adults are more likely to be canceled.
Children (children): Similar to adults, the positive coefficient (0.2169) indicates that bookings with more children have a higher chance of being canceled.
Babies (babies): The negative coefficient (-0.1807) suggests that bookings including babies are less likely to be canceled, though the effect is relatively smaller compared to other predictors.
Hotel Type (hotelResort Hotel): The negative coefficient (-0.5707) for resort hotels implies that bookings at resort hotels are less likely to be canceled compared to city hotels.
Statistical Significance:
- All predictors are statistically significant (p < 0.05), as indicated by the Pr(>|z|) values. This suggests that these factors are important in predicting the likelihood of cancellation.
Model Fit:
- The null deviance and residual deviance indicate how well the model fits the data. The decrease in deviance from the null model (no predictors) to the current model suggests an improvement in fit.
- The AIC (Akaike Information Criterion) value provides a measure for model comparison; lower values generally indicate a better model fit, taking into account the number of predictors.
Implications for Hotel Management
Lead Time: Hotels might consider strategies to manage long lead times, such as flexible cancellation policies or incentives for early confirmation.
Special Requests: Encouraging guests to make special requests could help reduce cancellations. Hotels could proactively communicate with guests to facilitate this.
Demographics: Tailoring services to families with children or groups with more adults, as they have higher cancellation probabilities.
Hotel Type: Different strategies may be needed for city hotels versus resort hotels due to their differing cancellation likelihoods.
Additional Considerations
Model Limitations: While significant, the model’s predictors only provide a partial view. Other factors not included in the model may also influence cancellation rates.
Further Analysis: Explore interactions between variables or include additional predictors like seasonality, reason for travel, or customer loyalty.
Model Validation: It’s crucial to validate the model on a separate test set to assess its predictive performance.
Operational Implementation: Consider how to integrate these insights into operational strategies and decision-making processes.
Model Evaluation
Split Data: Divide the data into training and testing sets to validate the model’s performance.
Model Training and Testing:
# Split the data
set.seed(123) # for reproducibility
training_indices <- sample(1:nrow(hotel_data), 0.8 * nrow(hotel_data))
train_data <- hotel_data[training_indices, ]
test_data <- hotel_data[-training_indices, ]
# Rebuild the model using training data
model_train <- update(model, data = train_data)
# Make Predictions on Test Data
predictions <- predict(model_train, newdata = test_data, type = "response")
predicted_class <- ifelse(predictions > 0.5, 1, 0)
# Confusion Matrix
confusion_matrix <- table(Predicted = predicted_class, Actual = test_data$is_canceled)
print(confusion_matrix)## Actual
## Predicted 0 1
## 0 13109 5407
## 1 1838 3488Model Evaluation - Confusion Matrix Analysis
The confusion matrix helps in evaluating the performance of the model. Let’s break down the confusion matrix:
True Negatives (TN): 13,109
This is the count of instances where your model correctly predicted the negative class (i.e., bookings that were not canceled).False Negatives (FN): 5,407
These are the instances where the model incorrectly predicted the negative class (i.e., the model predicted that the bookings would not be canceled, but they were actually canceled).False Positives (FP): 1,838
This is the count of instances where the model incorrectly predicted the positive class (i.e., the model predicted that the bookings would be canceled, but they were not).True Positives (TP): 3,488
These are the instances where the model correctly predicted the positive class (i.e., bookings that were canceled).
# Confusion Matrix Values
TP <- 3488 # True Positives
TN <- 13109 # True Negatives
FP <- 1838 # False Positives
FN <- 5407 # False Negatives
# Calculating performance metrics
accuracy <- (TP + TN) / (TP + TN + FP + FN)
precision <- TP / (TP + FP)
recall <- TP / (TP + FN)
f1_score <- 2 * (precision * recall) / (precision + recall)
# Print the metrics
print(paste("Accuracy:", accuracy))## [1] "Accuracy: 0.696124486200822"print(paste("Precision:", precision))## [1] "Precision: 0.654900488171235"print(paste("Recall:", recall))## [1] "Recall: 0.392130410342889"print(paste("F1 Score:", f1_score))## [1] "F1 Score: 0.490542155966528"Conclusions
Based on the results from the performance metrics calculation of your logistic regression model, here are the interpretations:
Accuracy (0.6961): This metric indicates that approximately 69.61% of the predictions made by your model are correct. It’s a measure of how well the model performs across both classes (canceled and not canceled).
Precision (0.6549): Precision is about 65.49%, showing the proportion of positive identifications (cancellations in this case) that were actually correct. A higher precision rate means fewer false positives.
Recall (0.3921): Recall, or sensitivity, is at 39.21%. This measures how many of the actual positive cases (cancellations) your model correctly predicted. In this context, it indicates that the model captures about 39.21% of all the cancellations.
F1 Score (0.4905): The F1 Score is a balance between precision and recall, calculated as their harmonic mean. An F1 Score of 0.4905 suggests a moderate balance between precision and recall, indicating that the model is neither heavily biased towards precision nor recall.
Interpretation and Implications
Moderate Accuracy: While the model correctly predicts cancellations and non-cancellations with reasonable accuracy, there is still room for improvement. Factors contributing to incorrect predictions should be examined.
Higher Precision than Recall: The model is more reliable in its positive predictions (cancellations) than in identifying all actual cancellations. This might be preferable in scenarios where false positives (predicting a cancellation that doesn’t happen) are more costly or disruptive than false negatives (missing a cancellation).
Low Recall: The model misses a significant portion of actual cancellations. This could be critical if missing out on actual cancellations is undesirable. For example, if the hotel depends on these predictions for managing bookings and logistics, a low recall might lead to inefficiencies.
Moderate F1 Score: This score reflects a balance between precision and recall but indicates that there is a need for improvement in both metrics.
Future Steps
Feature Engineering: Review and potentially add new features that could improve the model’s performance, such as guest demographics, seasonal factors, or historical booking trends.
Model Tuning: Adjust the model’s parameters or try different modeling techniques to see if the performance can be improved.
Data Quality: Ensure the quality of the data used for training the model. Inaccurate or biased data can significantly impact the model’s performance.
Advanced Models: Consider exploring more complex models or machine learning techniques which might capture the nuances in the data better.
Summary:
- The model has moderate accuracy and a balanced but moderate F1 Score. This indicates an overall decent performance but highlights potential areas for improvement, particularly in recall.
- The model is more effective at avoiding false positives than at capturing all true positives (actual cancellations). Depending on the specific operational needs and costs associated with false negatives (missed cancellations) versus false positives (wrongly predicted cancellations), different strategies may be required to fine-tune the model.
- Future steps could involve exploring different feature sets, trying out other modeling techniques, or adjusting the current model’s parameters to improve recall without significantly sacrificing precision.
Exploratory Data Analysis (EDA)
- Distribution of Lead Time for Canceled and Not Canceled Bookings
# Histogram of Lead Time for Canceled Bookings
ggplot(hotel_data, aes(x = lead_time)) +
geom_histogram(binwidth = 10, fill = "red", alpha = 0.5) +
facet_wrap(~is_canceled) +
labs(title = "Distribution of Lead Time for Canceled and Not Canceled Bookings", x = "Lead Time", y = "Frequency")
- Relationship between Special Requests and Cancellation
# Bar Plot of Special Requests vs. Cancellation
ggplot(hotel_data, aes(x = as.factor(total_of_special_requests), fill = as.factor(is_canceled))) +
geom_bar(position = "fill") +
labs(title = "Proportion of Cancellations by Number of Special Requests", x = "Number of Special Requests", y = "Proportion", fill = "Canceled")
# T-Test for checking if mean lead time is significantly different for canceled vs. non-canceled bookings
lead_time_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 1]
lead_time_not_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 0]
t_test_result <- t.test(lead_time_canceled, lead_time_not_canceled)
print(t_test_result)##
## Welch Two Sample t-test
##
## data: lead_time_canceled and lead_time_not_canceled
## t = 98.936, df = 74953, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 63.52042 66.08805
## sample estimates:
## mean of x mean of y
## 144.88626 80.08203Understanding the Insights:
t-value: The t-value is 98.936. This value indicates how many standard errors the sample mean of canceled bookings (144.88626) is away from the sample mean of non-canceled bookings (80.08203). A higher t-value suggests a greater difference between the means.
df (degrees of freedom): The degrees of freedom for this test are 74953. It’s a measure of the variability in the data. In this case, it’s a very large value because you have a large number of data points.
p-value: The p-value is less than 2.2e-16, which is an extremely small value. This indicates that there is strong evidence to reject the null hypothesis. In other words, the mean lead time for canceled bookings is indeed significantly different from the mean lead time for non-canceled bookings.
Confidence Interval: The 95 percent confidence interval for the difference in means is between 63.52042 and 66.08805. This interval provides a range of values within which we can be 95 percent confident that the true difference in means lies. It does not include 0, which further supports the conclusion that the means are significantly different.
Insights:
The results of the t-test suggest that there is a statistically significant difference in the mean lead time between canceled and non-canceled bookings. Specifically, the mean lead time for canceled bookings (approximately 144.89) is significantly higher than that for non-canceled bookings (approximately 80.08).
Predictive Modelling
Objective: To develop a model that can predict an outcome (e.g., cancellation).
Example: Logistic Regression Model for predicting cancellation.
# Building the model
model <- glm(is_canceled ~ lead_time + total_of_special_requests + adults + children + babies + hotel, data = hotel_data, family = "binomial")
# Summary of the model
summary(model)##
## Call:
## glm(formula = is_canceled ~ lead_time + total_of_special_requests +
## adults + children + babies + hotel, family = "binomial",
## data = hotel_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.171e+00 2.659e-02 -44.057 <2e-16 ***
## lead_time 5.425e-03 6.454e-05 84.059 <2e-16 ***
## total_of_special_requests -7.271e-01 9.863e-03 -73.722 <2e-16 ***
## adults 3.109e-01 1.375e-02 22.622 <2e-16 ***
## children 2.169e-01 1.599e-02 13.567 <2e-16 ***
## babies -1.807e-01 8.548e-02 -2.114 0.0345 *
## hotelResort Hotel -5.707e-01 1.421e-02 -40.175 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 157205 on 119209 degrees of freedom
## Residual deviance: 138576 on 119203 degrees of freedom
## AIC: 138590
##
## Number of Fisher Scoring iterations: 4Coefficients
The coefficients for each predictor variable indicate their impact on the log-odds of a booking being canceled (is_canceled = 1). For example:
- lead_time: A positive coefficient (0.005425) suggests that as lead time increases, the log-odds of a booking being canceled also increase.
- total_of_special_requests: A negative coefficient (-0.7271) implies that more special requests are associated with a lower log-odds of cancellation.
- adults, children, and babies: Positive coefficients suggest that bookings with more adults, children, or babies have a higher log-odds of being canceled.
- hotelResort Hotel: A negative coefficient (-0.5707) for resort hotels indicates that bookings at resort hotels are less likely to be canceled compared to city hotels.
Statistical Significance
The “z value” and “Pr(>|z|)” columns provide information about the statistical significance of each coefficient. All predictors are highly statistically significant (p < 0.001), as indicated by the very low p-values. This suggests that these factors are important in predicting the likelihood of cancellation.
Model Fit
The model’s null deviance and residual deviance indicate how well the model fits the data. The decrease in deviance from the null model (no predictors) to the current model suggests an improvement in fit. A lower AIC (Akaike Information Criterion) value generally indicates a better model fit, considering the number of predictors.
Implications for Hotel Management
Based on the coefficients, you can derive insights for hotel management:
- Lead Time: Strategies to manage long lead times, such as flexible cancellation policies or incentives for early confirmation, could help reduce cancellations.
- Special Requests: Encouraging guests to make special requests could reduce cancellations. Proactive communication with guests to facilitate special requests might be beneficial.
- Demographics: Tailoring services to families with children or groups with more adults, as they have higher cancellation probabilities.
- Hotel Type: Different strategies may be needed for city hotels versus resort hotels due to their differing cancellation likelihoods.
Additional Considerations
The result reminds you of the limitations and future steps:
- Model Limitations: The predictors in the model provide a partial view of the factors influencing cancellation rates. Other unmeasured factors may also play a role.
- Further Analysis: Consider exploring interactions between variables or including additional predictors like seasonality, reasons for travel, or customer loyalty.
- Model Validation: It’s crucial to validate the model on a separate test set to assess its predictive performance.
- Operational Implementation: Consider how to integrate these insights into operational strategies and decision-making processes.
In summary, the logistic regression model provides valuable insights into factors influencing booking cancellations. The next steps may involve model validation, fine-tuning, and operational implementation of strategies based on the findings. Additionally, further analysis and exploration of other variables can lead to a more comprehensive understanding of cancellation patterns.
Model Evaluation
Objective: To assess the accuracy and effectiveness of the predictive model.
Example: Using a Confusion Matrix to evaluate the model.
# Split data into training and test sets
set.seed(123)
training_indices <- sample(1:nrow(hotel_data), 0.8 * nrow(hotel_data))
train_data <- hotel_data[training_indices, ]
test_data <- hotel_data[-training_indices, ]
# Rebuild the model using training data
model_train <- glm(is_canceled ~ lead_time + total_of_special_requests + adults + children + babies + hotel, data = train_data, family = "binomial")
# Predict on test data
predictions <- predict(model_train, newdata = test_data, type = "response")
predicted_class <- ifelse(predictions > 0.5, 1, 0)
# Confusion Matrix
table(Predicted = predicted_class, Actual = test_data$is_canceled)## Actual
## Predicted 0 1
## 0 13109 5407
## 1 1838 3488Hypothesis Testing Results
Lead Time and Cancellation: There is a statistically significant difference in the mean lead time between canceled and non-canceled bookings. The t-test result indicates that bookings that were canceled had a higher average lead time compared to those that were not canceled. This suggests that longer lead times are associated with a higher likelihood of cancellation.
Special Requests and Cancellation: The t-test shows a significant difference in the mean number of special requests between canceled and non-canceled bookings. Bookings that were not canceled had a higher number of special requests on average, indicating that guests with more special requests are less likely to cancel their bookings.
Predictive Modeling Results
Logistic Regression Model: The model aimed to predict cancellations based on various features like lead time, the number of special requests, the number of adults, children, and babies, and the type of hotel. Significant predictors included: - Lead Time: Positive relationship with cancellation; longer lead times increase the likelihood of cancellation. - Total of Special Requests: Negative relationship; more special requests decrease the likelihood of cancellation. - Adults: Positive relationship; more adults increase the likelihood of cancellation. - Children: Positive relationship; more children slightly increase the likelihood of cancellation. - Babies: Negative relationship; the presence of babies slightly decreases the likelihood of cancellation. - Hotel Type (Resort Hotel): Negative relationship; bookings in resort hotels are less likely to be canceled compared to city hotels.
Model Evaluation Results
Confusion Matrix: The predictive model’s performance was evaluated using a confusion matrix: - True Negatives (TN): 13,109 (correctly predicted non-cancellations) - False Positives (FP): 1,838 (incorrectly predicted cancellations) - False Negatives (FN): 5,407 (incorrectly predicted non-cancellations) - True Positives (TP): 3,488 (correctly predicted cancellations)
Overall Conclusions
Lead Time: Monitoring and managing lead times could be a critical strategy in reducing cancellation rates. Offering incentives for early confirmations or implementing stricter cancellation policies for long lead times might be beneficial.
Special Requests: Encouraging guests to make special requests could be a strategy to reduce cancellation rates. This could involve proactive communication from the hotel to inquire about any additional needs or preferences of the guests.
Target Audience: Tailoring services and marketing strategies towards adults and families with children, who are more likely to cancel, could help in reducing the cancellation rate.
Hotel Type Consideration: Different strategies might be needed for city hotels and resort hotels, given their different cancellation patterns.
Predictive Model Usage: The logistic regression model can be used as a tool for predicting the likelihood of cancellations, which can assist in making informed decisions and strategies for hotel management.
Future Research: Further analysis could explore other variables not included in the model, such as seasonal trends, reasons for cancellation, and guest reviews. Additionally, more advanced machine learning techniques could be employed to improve predictive performance.
Hypothesis Testing
- Let’s test a hypothesis: “Bookings with longer lead times are more likely to be canceled.”
# Split data into canceled and not canceled for lead time
lead_time_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 1]
lead_time_not_canceled <- hotel_data$lead_time[hotel_data$is_canceled == 0]
# Perform a t-test
t_test_result <- t.test(lead_time_canceled, lead_time_not_canceled)
print(t_test_result)##
## Welch Two Sample t-test
##
## data: lead_time_canceled and lead_time_not_canceled
## t = 98.936, df = 74953, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 63.52042 66.08805
## sample estimates:
## mean of x mean of y
## 144.88626 80.08203Conclusions
Statistical Significance: The p-value associated with the t-test is much less than 0.05 (typically considered the threshold for statistical significance), and it is expressed as p-value < 2.2e-16. This extremely small p-value indicates strong evidence to reject the null hypothesis. In other words, there is a statistically significant difference in the mean lead time between canceled and non-canceled bookings.
Effect Size: The t-value is 98.936, which is a measure of the magnitude of the difference between the means of the two groups. A higher t-value suggests a greater difference between the means. In this case, the large t-value indicates a substantial difference in mean lead times.
Confidence Interval: The 95 percent confidence interval for the difference in means is between 63.52042 and 66.08805. This interval provides a range of values within which we can be 95 percent confident that the true difference in means lies. Importantly, it does not include 0, which further supports the conclusion that the means are significantly different.
Mean Values: The sample estimates for the mean lead times are as follows:
- Mean lead time for canceled bookings (mean of x): 144.88626
- Mean lead time for non-canceled bookings (mean of y): 80.08203
These mean values indicate that, on average, bookings that were canceled had a substantially higher lead time compared to those that were not canceled.
In summary, the t-test results suggest a statistically significant and practically meaningful difference in mean lead times between canceled and non-canceled bookings. Bookings that were canceled had, on average, a significantly longer lead time compared to bookings that were not canceled. This insight can be valuable for hotel management in understanding the relationship between lead times and booking cancellations.
Building Predictive Model
- I’ll create a logistic regression model to predict cancellations.
# Logistic Regression Model
model <- glm(is_canceled ~ lead_time + adults + children + babies + hotel, data = hotel_data, family = "binomial")
# Summary of the model
summary(model)##
## Call:
## glm(formula = is_canceled ~ lead_time + adults + children + babies +
## hotel, family = "binomial", data = hotel_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.220e+00 2.576e-02 -47.344 < 2e-16 ***
## lead_time 5.717e-03 6.261e-05 91.320 < 2e-16 ***
## adults 1.315e-01 1.306e-02 10.068 < 2e-16 ***
## children 1.182e-01 1.552e-02 7.616 2.62e-14 ***
## babies -7.283e-01 8.639e-02 -8.431 < 2e-16 ***
## hotelResort Hotel -5.793e-01 1.386e-02 -41.798 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 157205 on 119209 degrees of freedom
## Residual deviance: 144949 on 119204 degrees of freedom
## AIC: 144961
##
## Number of Fisher Scoring iterations: 4summary
- This logistic regression model provides insights into the relationship between various predictor variables (lead_time, adults, children, babies, and hotel type) and the likelihood of booking cancellations. The coefficients and their significance levels indicate which factors have a significant impact on whether a booking is canceled or not. The model fits the data well, and it can be used to make predictions regarding cancellations based on these factors.
Model Evaluation
- Evaluate the model using a test dataset.
# Split the dataset into training and testing sets
set.seed(123)
training_indices <- sample(1:nrow(hotel_data), 0.8 * nrow(hotel_data))
train_data <- hotel_data[training_indices, ]
test_data <- hotel_data[-training_indices, ]
# Build the model using training data
model_train <- glm(is_canceled ~ lead_time + adults + children + babies + hotel, data = train_data, family = "binomial")
# Make predictions on the test data
predictions <- predict(model_train, newdata = test_data, type = "response")
predicted_class <- ifelse(predictions > 0.5, 1, 0)
# Confusion Matrix
confusionMatrix <- table(Predicted = predicted_class, Actual = test_data$is_canceled)
print(confusionMatrix)## Actual
## Predicted 0 1
## 0 13178 6318
## 1 1769 2577Additional Data Exploration:
Time Series Analysis:
- Since I have data on the arrival date, I will explore trends over time. For example, analyze booking patterns by month or year.
hotel_data$arrival_date <- as.Date(with(hotel_data, paste(arrival_date_year, arrival_date_month, arrival_date_day_of_month, sep="-")), format="%Y-%B-%d")
ggplot(hotel_data, aes(x = arrival_date)) + geom_line(stat = "count") + labs(title = "Booking Trends Over Time", x = "Date", y = "Number of Bookings")
# Geographical Analysis:
- I have data on the guests’ countries, considering visualizing the geographical distribution of your guests.
library(ggplot2)
ggplot(hotel_data, aes(x = country)) + geom_bar() + coord_flip() + labs(title = "Guest Distribution by Country", x = "Country", y = "Count")
# Create a month-year column for seasonal analysis
hotel_data$month_year <- format(as.Date(hotel_data$arrival_date), "%Y-%m")
# Aggregate data by month-year
seasonal_data <- hotel_data %>%
group_by(month_year) %>%
summarise(total_bookings = n(),
canceled_bookings = sum(is_canceled))
# Plotting seasonal trends in bookings
ggplot(seasonal_data, aes(x = month_year, y = total_bookings, fill = canceled_bookings)) +
geom_bar(stat = "identity") +
labs(title = "Seasonal Booking Trends", x = "Month-Year", y = "Total Bookings") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
library(randomForest)## Warning: package 'randomForest' was built under R version 4.3.2## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:gridExtra':
##
## combine## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:dplyr':
##
## combine# Ensure 'is_canceled' is a factor for classification
train_data$is_canceled <- as.factor(train_data$is_canceled)
test_data$is_canceled <- as.factor(test_data$is_canceled)
# Build the Random Forest classification model
rf_model <- randomForest(is_canceled ~ lead_time + total_of_special_requests +
adults + children + babies + hotel,
data = train_data)
# Summary of the model
print(rf_model)##
## Call:
## randomForest(formula = is_canceled ~ lead_time + total_of_special_requests + adults + children + babies + hotel, data = train_data)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 2
##
## OOB estimate of error rate: 28.03%
## Confusion matrix:
## 0 1 class.error
## 0 49404 10660 0.1774774
## 1 16072 19232 0.4552459# Make predictions on the test data
rf_predictions <- predict(rf_model, newdata = test_data, type = "class")
# Generate a confusion matrix
rf_confusion_matrix <- table(Predicted = rf_predictions, Actual = test_data$is_canceled)
print(rf_confusion_matrix)## Actual
## Predicted 0 1
## 0 12353 3996
## 1 2594 4899Inferences
The Random Forest model demonstrates some predictive capability, but there is room for improvement, especially in correctly identifying canceled bookings (class 1).
The OOB error rate of 28.07% indicates that the model may not generalize perfectly to unseen data and could benefit from further tuning or feature engineering.
The class error rate for class 1 (canceled bookings) is notably higher than that for class 0 (non-canceled bookings), indicating that the model struggles more with predicting cancellations accurately.
Depending on the specific requirements and business context, you may want to explore techniques for improving the model’s performance, such as hyperparameter tuning, feature selection, or addressing class imbalance if present in the dataset. Additionally, considering alternative machine learning algorithms or ensemble methods may also be beneficial.
confusion matrix indicates:
- True Negatives (TN): 12,350 bookings were correctly predicted as not canceled.
- True Positives (TP): 4,899 bookings were correctly predicted as canceled.
- False Negatives (FN): 3,996 bookings were incorrectly predicted as not canceled (but they were actually canceled).
- False Positives (FP): 2,597 bookings were incorrectly predicted as canceled (but they were actually not canceled).
From this confusion matrix, you can calculate several performance metrics:
- Accuracy: The proportion of all predictions that are correct. It’s calculated as (TP + TN) / Total Predictions.
- Precision: The proportion of positive identifications that were actually correct. Calculated as TP / (TP + FP).
- Recall (Sensitivity): The proportion of actual positives that were identified correctly. Calculated as TP / (TP + FN).
- F1 Score: The harmonic mean of precision and recall. Calculated as 2 * (Precision * Recall) / (Precision + Recall).
These metrics will give you a comprehensive understanding of your model’s performance. Let’s calculate these metrics:
# Calculating performance metrics
TP <- 4899
TN <- 12350
FP <- 2597
FN <- 3996
accuracy <- (TP + TN) / (TP + TN + FP + FN)
precision <- TP / (TP + FP)
recall <- TP / (TP + FN)
f1_score <- 2 * (precision * recall) / (precision + recall)
# Print the metrics
print(paste("Accuracy:", accuracy))## [1] "Accuracy: 0.723471185303246"print(paste("Precision:", precision))## [1] "Precision: 0.65354855923159"print(paste("Recall:", recall))## [1] "Recall: 0.550758853288364"print(paste("F1 Score:", f1_score))## [1] "F1 Score: 0.597767067293027"The performance metrics for your Random Forest model are as follows:
Accuracy: 72.35% - This means that your model correctly predicts whether a booking will be canceled or not about 72.35% of the time. It’s a good measure of overall performance, but it’s also important to look at the other metrics to understand the model’s strengths and weaknesses in specific areas.
Precision: 65.35% - Precision measures the accuracy of the positive predictions. In your case, when your model predicts that a booking will be canceled, it’s correct 65.35% of the time. This is a useful metric to assess the reliability of positive predictions (cancellations).
Recall: 55.08% - Recall, or sensitivity, indicates that your model correctly identifies 55.08% of all actual cancellations. This is important in scenarios where missing out on actual cancellations (false negatives) could be costly or problematic.
F1 Score: 59.78% - The F1 Score balances precision and recall. An F1 Score of 59.78% suggests a moderate balance between the precision and recall of your model. It’s often more informative than accuracy, especially if the class distribution is imbalanced.
Interpretation and Implications: Moderate Accuracy and F1 Score: My model has a decent overall accuracy and a moderate F1 Score, indicating balanced precision and recall. However, there is room for improvement, especially in correctly identifying more true positives (actual cancellations).
Precision and Recall: The precision of over 65% is relatively good, indicating fewer false positives. However, the recall of about 55% suggests that the model misses nearly half of the actual cancellations. Depending on the business context, this might be an area to focus on for improvement.
Model Usage: The model can be used to predict cancellations, but I might want to improve its recall to capture more actual cancellations, depending on how critical false negatives are for your operation.
Improving the Model: I would try different algorithms, tune the parameters of your Random Forest model, or explore feature engineering to improve the model’s recall without significantly sacrificing precision.
Conclusion:
- The model performs adequately, but there’s potential to enhance its ability to detect cancellations more effectively, depending on the specific requirements and tolerances of your hotel management strategy.
#OVERALL cONCLUSION:
Hypothesis-3
Test Details
- The t-test compares the mean lead time (the number of days between
booking and arrival) for canceled bookings
(
lead_time_canceled) and not canceled bookings (lead_time_not_canceled).
Statistical Results
- t-Value: 98.936
A higher t-value indicates a more significant difference between the two groups. - Degrees of Freedom: Approximately 74953
Derived based on the sample sizes of the two groups and their variances. - P-Value: Less than 2.2e-16
Extremely small, indicating a statistically significant difference. A p-value less than 0.05 is typically considered significant. - Confidence Interval: 63.52042 to 66.08805
The 95% confidence interval for the difference in means. I can be 95% confident that the true difference in mean lead times lies within this range.
Sample Estimates
- Mean of x (canceled bookings): 144.88626
- Mean of y (not canceled bookings): 80.08203
On average, the lead time for canceled bookings is much higher than for those that are not canceled.
Interpretation and Implications
- Lead Time as an Indicator: The significant difference in lead times suggests that bookings made well in advance are more likely to be canceled than those made closer to the arrival date. This could be due to various factors like changes in plans, finding better deals, or uncertainty in long-term planning.
- Strategic Implications: Hotels may use this information to modify their booking and cancellation policies. For example, implementing non-refundable deposits for early bookings or offering discounts for last-minute bookings to reduce the number of cancellations.
- Further Exploration: Exploring if this trend holds across different types of hotels (e.g., city vs. resort) or during different seasons could provide more insights.
Hypothesis-4
Interpretation
- The negative t-value (-88.899) and the extremely small p-value (< 2.2e-16) indicate a statistically significant difference between the two groups. The mean number of special requests for canceled bookings (mean of x) is 0.3288988, significantly lower than that for not canceled bookings (mean of y), which is 0.7144552. This result supports the hypothesis that bookings with more special requests are less likely to be canceled.
Conclusions:
- Special Requests Impact: Bookings with fewer special requests are more likely to be canceled. Conversely, more special requests are associated with a lower likelihood of cancellation.
- Implications for Hotel Management: Encouraging guests to make special requests (e.g., room preferences, dietary needs) might lead to a reduction in cancellation rates. This could be achieved through targeted communication and offers.
- Further Analysis: This result could be explored further to understand if certain types of special requests are more influential in preventing cancellations or if the effect varies by other factors like season, hotel type, or customer segment.
Q1.Are the Guest with children need parking space then guest with no children?(Multivariate)
#----------------------------------- Questions to Answer -------------------------------------------
hotel_stays <- hotel_data %>%
filter(is_canceled == 0) %>%
mutate(
children = case_when(
children + babies > 0 ~ "children",
TRUE ~ "none"
),
required_car_parking_spaces = case_when(
required_car_parking_spaces > 0 ~ "parking",
TRUE ~ "none"
)
)#%>%
#select(-is_canceled, -reservation_status, -babies)
hotel_stays## hotel is_canceled lead_time arrival_date_year arrival_date_month
## 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
## 7 Resort Hotel 0 0 2015 July
## 8 Resort Hotel 0 9 2015 July
## 9 Resort Hotel 0 35 2015 July
## 10 Resort Hotel 0 68 2015 July
## 11 Resort Hotel 0 18 2015 July
## 12 Resort Hotel 0 37 2015 July
## 13 Resort Hotel 0 68 2015 July
## 14 Resort Hotel 0 37 2015 July
## 15 Resort Hotel 0 12 2015 July
## 16 Resort Hotel 0 0 2015 July
## 17 Resort Hotel 0 7 2015 July
## 18 Resort Hotel 0 37 2015 July
## 19 Resort Hotel 0 72 2015 July
## 20 Resort Hotel 0 72 2015 July
## 21 Resort Hotel 0 72 2015 July
## 22 Resort Hotel 0 127 2015 July
## 23 Resort Hotel 0 78 2015 July
## 24 Resort Hotel 0 48 2015 July
## 25 Resort Hotel 0 77 2015 July
## 26 Resort Hotel 0 99 2015 July
## 27 Resort Hotel 0 118 2015 July
## 28 Resort Hotel 0 95 2015 July
## 29 Resort Hotel 0 69 2015 July
## 30 Resort Hotel 0 15 2015 July
## 31 Resort Hotel 0 36 2015 July
## 32 Resort Hotel 0 70 2015 July
## 33 Resort Hotel 0 45 2015 July
## 34 Resort Hotel 0 16 2015 July
## 35 Resort Hotel 0 70 2015 July
## 36 Resort Hotel 0 107 2015 July
## 37 Resort Hotel 0 96 2015 July
## 38 Resort Hotel 0 113 2015 July
## 39 Resort Hotel 0 90 2015 July
## 40 Resort Hotel 0 50 2015 July
## 41 Resort Hotel 0 113 2015 July
## 42 Resort Hotel 0 93 2015 July
## 43 Resort Hotel 0 76 2015 July
## 44 Resort Hotel 0 3 2015 July
## 45 Resort Hotel 0 1 2015 July
## 46 Resort Hotel 0 1 2015 July
## 47 Resort Hotel 0 0 2015 July
## 48 Resort Hotel 0 0 2015 July
## 49 Resort Hotel 0 0 2015 July
## 50 Resort Hotel 0 14 2015 July
## 51 Resort Hotel 0 10 2015 July
## 52 Resort Hotel 0 5 2015 July
## 53 Resort Hotel 0 17 2015 July
## 54 Resort Hotel 0 93 2015 July
## 55 Resort Hotel 0 10 2015 July
## 56 Resort Hotel 0 3 2015 July
## 57 Resort Hotel 0 51 2015 July
## 58 Resort Hotel 0 2 2015 July
## 59 Resort Hotel 0 15 2015 July
## 60 Resort Hotel 0 3 2015 July
## 61 Resort Hotel 0 2 2015 July
## 62 Resort Hotel 0 72 2015 July
## 63 Resort Hotel 0 81 2015 July
## 64 Resort Hotel 0 99 2015 July
## 65 Resort Hotel 0 364 2015 July
## 66 Resort Hotel 0 99 2015 July
## 67 Resort Hotel 0 324 2015 July
## 68 Resort Hotel 0 69 2015 July
## 69 Resort Hotel 0 12 2015 July
## 70 Resort Hotel 0 9 2015 July
## 71 Resort Hotel 0 1 2015 July
## 72 Resort Hotel 0 21 2015 July
## 73 Resort Hotel 0 9 2015 July
## 74 Resort Hotel 0 109 2015 July
## 75 Resort Hotel 0 63 2015 July
## 76 Resort Hotel 0 101 2015 July
## 77 Resort Hotel 0 102 2015 July
## 78 Resort Hotel 0 4 2015 July
## 79 Resort Hotel 0 98 2015 July
## 80 Resort Hotel 0 92 2015 July
## 81 Resort Hotel 0 95 2015 July
## 82 Resort Hotel 0 102 2015 July
## 83 Resort Hotel 0 115 2015 July
## 84 Resort Hotel 0 102 2015 July
## 85 Resort Hotel 0 86 2015 July
## 86 Resort Hotel 0 78 2015 July
## 87 Resort Hotel 0 52 2015 July
## 88 Resort Hotel 0 29 2015 July
## 89 Resort Hotel 0 109 2015 July
## 90 Resort Hotel 0 92 2015 July
## 91 Resort Hotel 0 92 2015 July
## 92 Resort Hotel 0 79 2015 July
## 93 Resort Hotel 0 2 2015 July
## 94 Resort Hotel 0 30 2015 July
## 95 Resort Hotel 0 32 2015 July
## 96 Resort Hotel 0 8 2015 July
## 97 Resort Hotel 0 100 2015 July
## 98 Resort Hotel 0 1 2015 July
## 99 Resort Hotel 0 100 2015 July
## 100 Resort Hotel 0 100 2015 July
## 101 Resort Hotel 0 9 2015 July
## 102 Resort Hotel 0 0 2015 July
## 103 Resort Hotel 0 9 2015 July
## 104 Resort Hotel 0 2 2015 July
## 105 Resort Hotel 0 2 2015 July
## 106 Resort Hotel 0 9 2015 July
## 107 Resort Hotel 0 2 2015 July
## 108 Resort Hotel 0 44 2015 July
## 109 Resort Hotel 0 80 2015 July
## 110 Resort Hotel 0 97 2015 July
## 111 Resort Hotel 0 109 2015 July
## 112 Resort Hotel 0 30 2015 July
## 113 Resort Hotel 0 64 2015 July
## 114 Resort Hotel 0 39 2015 July
## 115 Resort Hotel 0 90 2015 July
## 116 Resort Hotel 0 97 2015 July
## 117 Resort Hotel 0 29 2015 July
## 118 Resort Hotel 0 27 2015 July
## 119 Resort Hotel 0 8 2015 July
## 120 Resort Hotel 0 82 2015 July
## 121 Resort Hotel 0 94 2015 July
## 122 Resort Hotel 0 110 2015 July
## 123 Resort Hotel 0 96 2015 July
## 124 Resort Hotel 0 79 2015 July
## 125 Resort Hotel 0 115 2015 July
## 126 Resort Hotel 0 111 2015 July
## 127 Resort Hotel 0 51 2015 July
## 128 Resort Hotel 0 94 2015 July
## 129 Resort Hotel 0 81 2015 July
## 130 Resort Hotel 0 84 2015 July
## 131 Resort Hotel 0 51 2015 July
## 132 Resort Hotel 0 111 2015 July
## 133 Resort Hotel 0 0 2015 July
## 134 Resort Hotel 0 2 2015 July
## 135 Resort Hotel 0 7 2015 July
## 136 Resort Hotel 0 98 2015 July
## 137 Resort Hotel 0 95 2015 July
## 138 Resort Hotel 0 66 2015 July
## 139 Resort Hotel 0 104 2015 July
## 140 Resort Hotel 0 28 2015 July
## 141 Resort Hotel 0 34 2015 July
## 142 Resort Hotel 0 98 2015 July
## 143 Resort Hotel 0 104 2015 July
## 144 Resort Hotel 0 68 2015 July
## 145 Resort Hotel 0 258 2015 July
## 146 Resort Hotel 0 111 2015 July
## 147 Resort Hotel 0 112 2015 July
## 148 Resort Hotel 0 100 2015 July
## 149 Resort Hotel 0 33 2015 July
## 150 Resort Hotel 0 97 2015 July
## 151 Resort Hotel 0 80 2015 July
## 152 Resort Hotel 0 90 2015 July
## 153 Resort Hotel 0 111 2015 July
## 154 Resort Hotel 0 65 2015 July
## 155 Resort Hotel 0 55 2015 July
## 156 Resort Hotel 0 21 2015 July
## 157 Resort Hotel 0 8 2015 July
## 158 Resort Hotel 0 8 2015 July
## 159 Resort Hotel 0 0 2015 July
## 160 Resort Hotel 0 0 2015 July
## 161 Resort Hotel 0 0 2015 July
## 162 Resort Hotel 0 8 2015 July
## 163 Resort Hotel 0 66 2015 July
## 164 Resort Hotel 0 88 2015 July
## 165 Resort Hotel 0 0 2015 July
## 166 Resort Hotel 0 54 2015 July
## 167 Resort Hotel 0 112 2015 July
## 168 Resort Hotel 0 68 2015 July
## 169 Resort Hotel 0 7 2015 July
## 170 Resort Hotel 0 1 2015 July
## 171 Resort Hotel 0 5 2015 July
## 172 Resort Hotel 0 83 2015 July
## 173 Resort Hotel 0 0 2015 July
## 174 Resort Hotel 0 1 2015 July
## 175 Resort Hotel 0 113 2015 July
## 176 Resort Hotel 0 96 2015 July
## 177 Resort Hotel 0 394 2015 July
## 178 Resort Hotel 0 33 2015 July
## 179 Resort Hotel 0 82 2015 July
## 180 Resort Hotel 0 100 2015 July
## 181 Resort Hotel 0 100 2015 July
## 182 Resort Hotel 0 17 2015 July
## 183 Resort Hotel 0 68 2015 July
## 184 Resort Hotel 0 12 2015 July
## 185 Resort Hotel 0 78 2015 July
## 186 Resort Hotel 0 16 2015 July
## 187 Resort Hotel 0 14 2015 July
## 188 Resort Hotel 0 77 2015 July
## 189 Resort Hotel 0 90 2015 July
## 190 Resort Hotel 0 90 2015 July
## 191 Resort Hotel 0 90 2015 July
## 192 Resort Hotel 0 366 2015 July
## 193 Resort Hotel 0 249 2015 July
## 194 Resort Hotel 0 10 2015 July
## 195 Resort Hotel 0 22 2015 July
## 196 Resort Hotel 0 91 2015 July
## 197 Resort Hotel 0 23 2015 July
## 198 Resort Hotel 0 11 2015 July
## 199 Resort Hotel 0 3 2015 July
## 200 Resort Hotel 0 13 2015 July
## 201 Resort Hotel 0 108 2015 July
## 202 Resort Hotel 0 91 2015 July
## 203 Resort Hotel 0 37 2015 July
## 204 Resort Hotel 0 21 2015 July
## 205 Resort Hotel 0 106 2015 July
## 206 Resort Hotel 0 30 2015 July
## 207 Resort Hotel 0 30 2015 July
## 208 Resort Hotel 0 79 2015 July
## 209 Resort Hotel 0 21 2015 July
## 210 Resort Hotel 0 35 2015 July
## 211 Resort Hotel 0 78 2015 July
## 212 Resort Hotel 0 31 2015 July
## 213 Resort Hotel 0 78 2015 July
## 214 Resort Hotel 0 78 2015 July
## 215 Resort Hotel 0 44 2015 July
## 216 Resort Hotel 0 34 2015 July
## 217 Resort Hotel 0 87 2015 July
## 218 Resort Hotel 0 11 2015 July
## 219 Resort Hotel 0 115 2015 July
## 220 Resort Hotel 0 86 2015 July
## 221 Resort Hotel 0 41 2015 July
## 222 Resort Hotel 0 32 2015 July
## 223 Resort Hotel 0 34 2015 July
## 224 Resort Hotel 0 34 2015 July
## 225 Resort Hotel 0 44 2015 July
## 226 Resort Hotel 0 304 2015 July
## 227 Resort Hotel 0 117 2015 July
## 228 Resort Hotel 0 70 2015 July
## 229 Resort Hotel 0 110 2015 July
## 230 Resort Hotel 0 33 2015 July
## 231 Resort Hotel 0 33 2015 July
## 232 Resort Hotel 0 33 2015 July
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## 2512 Resort Hotel 0 136 2016 March
## 2513 Resort Hotel 0 162 2016 March
## 2514 Resort Hotel 0 162 2016 March
## 2515 Resort Hotel 0 162 2016 March
## 2516 Resort Hotel 0 162 2016 March
## 2517 Resort Hotel 0 136 2016 March
## 2518 Resort Hotel 0 136 2016 March
## 2519 Resort Hotel 0 136 2016 March
## 2520 Resort Hotel 0 136 2016 March
## 2521 Resort Hotel 0 136 2016 March
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## 2523 Resort Hotel 0 162 2016 March
## 2524 Resort Hotel 0 162 2016 March
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## 2526 Resort Hotel 0 136 2016 March
## 2527 Resort Hotel 0 136 2016 March
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## 2530 Resort Hotel 0 162 2016 March
## 2531 Resort Hotel 0 162 2016 March
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## 2534 Resort Hotel 0 136 2016 March
## 2535 Resort Hotel 0 136 2016 March
## 2536 Resort Hotel 0 136 2016 March
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## 2538 Resort Hotel 0 162 2016 March
## 2539 Resort Hotel 0 136 2016 March
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## 2541 Resort Hotel 0 192 2016 March
## 2542 Resort Hotel 0 192 2016 March
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## 2544 Resort Hotel 0 149 2016 March
## 2545 Resort Hotel 0 147 2016 March
## 2546 Resort Hotel 0 147 2016 March
## 2547 Resort Hotel 0 147 2016 March
## 2548 Resort Hotel 0 147 2016 March
## 2549 Resort Hotel 0 147 2016 March
## 2550 Resort Hotel 0 147 2016 March
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## 2557 Resort Hotel 0 168 2016 March
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## 2559 Resort Hotel 0 168 2016 March
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## 2571 Resort Hotel 0 169 2016 March
## 2572 Resort Hotel 0 169 2016 March
## 2573 Resort Hotel 0 169 2016 March
## 2574 Resort Hotel 0 169 2016 March
## 2575 Resort Hotel 0 169 2016 March
## 2576 Resort Hotel 0 169 2016 March
## 2577 Resort Hotel 0 169 2016 March
## 2578 Resort Hotel 0 169 2016 March
## 2579 Resort Hotel 0 169 2016 March
## 2580 Resort Hotel 0 169 2016 March
## 2581 Resort Hotel 0 169 2016 March
## 2582 Resort Hotel 0 169 2016 March
## 2583 Resort Hotel 0 169 2016 March
## 2584 Resort Hotel 0 169 2016 March
## 2585 Resort Hotel 0 169 2016 March
## 2586 Resort Hotel 0 169 2016 March
## 2587 Resort Hotel 0 169 2016 March
## 2588 Resort Hotel 0 169 2016 March
## 2589 Resort Hotel 0 169 2016 March
## 2590 Resort Hotel 0 169 2016 March
## 2591 Resort Hotel 0 169 2016 March
## 2592 Resort Hotel 0 169 2016 March
## 2593 Resort Hotel 0 169 2016 March
## 2594 Resort Hotel 0 169 2016 March
## 2595 Resort Hotel 0 169 2016 March
## 2596 Resort Hotel 0 169 2016 March
## 2597 Resort Hotel 0 169 2016 March
## 2598 Resort Hotel 0 169 2016 March
## 2599 Resort Hotel 0 169 2016 March
## 2600 Resort Hotel 0 169 2016 March
## 2601 Resort Hotel 0 169 2016 March
## 2602 Resort Hotel 0 169 2016 March
## 2603 Resort Hotel 0 169 2016 March
## 2604 Resort Hotel 0 169 2016 March
## 2605 Resort Hotel 0 169 2016 March
## 2606 Resort Hotel 0 169 2016 March
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## 2608 Resort Hotel 0 169 2016 March
## 2609 Resort Hotel 0 169 2016 March
## 2610 Resort Hotel 0 169 2016 March
## 2611 Resort Hotel 0 169 2016 March
## 2612 Resort Hotel 0 169 2016 March
## 2613 Resort Hotel 0 169 2016 March
## 2614 Resort Hotel 0 169 2016 March
## 2615 Resort Hotel 0 169 2016 March
## 2616 Resort Hotel 0 169 2016 March
## 2617 Resort Hotel 0 169 2016 March
## 2618 Resort Hotel 0 169 2016 March
## 2619 Resort Hotel 0 169 2016 March
## 2620 Resort Hotel 0 169 2016 March
## 2621 Resort Hotel 0 0 2016 April
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## 2623 Resort Hotel 0 144 2016 April
## 2624 Resort Hotel 0 144 2016 April
## 2625 Resort Hotel 0 163 2016 April
## 2626 Resort Hotel 0 175 2016 April
## 2627 Resort Hotel 0 222 2016 April
## 2628 Resort Hotel 0 222 2016 April
## 2629 Resort Hotel 0 0 2016 April
## 2630 Resort Hotel 0 1 2016 April
## 2631 Resort Hotel 0 182 2016 April
## 2632 Resort Hotel 0 171 2016 April
## 2633 Resort Hotel 0 171 2016 April
## 2634 Resort Hotel 0 171 2016 April
## 2635 Resort Hotel 0 171 2016 April
## 2636 Resort Hotel 0 171 2016 April
## 2637 Resort Hotel 0 171 2016 April
## 2638 Resort Hotel 0 171 2016 April
## 2639 Resort Hotel 0 171 2016 April
## 2640 Resort Hotel 0 171 2016 April
## 2641 Resort Hotel 0 171 2016 April
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## 2643 Resort Hotel 0 171 2016 April
## 2644 Resort Hotel 0 171 2016 April
## 2645 Resort Hotel 0 171 2016 April
## 2646 Resort Hotel 0 171 2016 April
## 2647 Resort Hotel 0 171 2016 April
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## 2665 Resort Hotel 0 171 2016 April
## 2666 Resort Hotel 0 171 2016 April
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## 2668 Resort Hotel 0 171 2016 April
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## 2673 Resort Hotel 0 200 2016 April
## 2674 Resort Hotel 0 200 2016 April
## 2675 Resort Hotel 0 210 2016 April
## 2676 Resort Hotel 0 149 2016 April
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## 2678 Resort Hotel 0 190 2016 April
## 2679 Resort Hotel 0 275 2016 April
## 2680 Resort Hotel 0 275 2016 April
## 2681 Resort Hotel 0 275 2016 April
## 2682 Resort Hotel 0 275 2016 April
## 2683 Resort Hotel 0 275 2016 April
## 2684 Resort Hotel 0 275 2016 April
## 2685 Resort Hotel 0 190 2016 April
## 2686 Resort Hotel 0 275 2016 April
## 2687 Resort Hotel 0 275 2016 April
## 2688 Resort Hotel 0 275 2016 April
## 2689 Resort Hotel 0 275 2016 April
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## 2691 Resort Hotel 0 190 2016 April
## 2692 Resort Hotel 0 275 2016 April
## 2693 Resort Hotel 0 275 2016 April
## 2694 Resort Hotel 0 275 2016 April
## 2695 Resort Hotel 0 0 2016 April
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## 2699 Resort Hotel 0 192 2016 April
## 2700 Resort Hotel 0 229 2016 April
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## 2702 Resort Hotel 0 183 2016 April
## 2703 Resort Hotel 0 183 2016 April
## 2704 Resort Hotel 0 183 2016 April
## 2705 Resort Hotel 0 183 2016 April
## 2706 Resort Hotel 0 183 2016 April
## 2707 Resort Hotel 0 183 2016 April
## 2708 Resort Hotel 0 183 2016 April
## 2709 Resort Hotel 0 183 2016 April
## 2710 Resort Hotel 0 183 2016 April
## 2711 Resort Hotel 0 183 2016 April
## 2712 Resort Hotel 0 183 2016 April
## 2713 Resort Hotel 0 183 2016 April
## 2714 Resort Hotel 0 183 2016 April
## 2715 Resort Hotel 0 183 2016 April
## 2716 Resort Hotel 0 183 2016 April
## 2717 Resort Hotel 0 183 2016 April
## 2718 Resort Hotel 0 183 2016 April
## 2719 Resort Hotel 0 183 2016 April
## 2720 Resort Hotel 0 183 2016 April
## 2721 Resort Hotel 0 183 2016 April
## 2722 Resort Hotel 0 183 2016 April
## 2723 Resort Hotel 0 183 2016 April
## 2724 Resort Hotel 0 183 2016 April
## 2725 Resort Hotel 0 183 2016 April
## 2726 Resort Hotel 0 183 2016 April
## 2727 Resort Hotel 0 183 2016 April
## 2728 Resort Hotel 0 183 2016 April
## 2729 Resort Hotel 0 183 2016 April
## 2730 Resort Hotel 0 169 2016 April
## 2731 Resort Hotel 0 169 2016 April
## 2732 Resort Hotel 0 192 2016 April
## 2733 Resort Hotel 0 200 2016 April
## 2734 Resort Hotel 0 218 2016 April
## 2735 Resort Hotel 0 185 2016 April
## 2736 Resort Hotel 0 185 2016 April
## 2737 Resort Hotel 0 185 2016 April
## 2738 Resort Hotel 0 185 2016 April
## 2739 Resort Hotel 0 185 2016 April
## 2740 Resort Hotel 0 185 2016 April
## 2741 Resort Hotel 0 185 2016 April
## 2742 Resort Hotel 0 185 2016 April
## 2743 Resort Hotel 0 185 2016 April
## 2744 Resort Hotel 0 185 2016 April
## 2745 Resort Hotel 0 185 2016 April
## 2746 Resort Hotel 0 185 2016 April
## 2747 Resort Hotel 0 185 2016 April
## 2748 Resort Hotel 0 185 2016 April
## 2749 Resort Hotel 0 185 2016 April
## 2750 Resort Hotel 0 185 2016 April
## 2751 Resort Hotel 0 185 2016 April
## 2752 Resort Hotel 0 185 2016 April
## 2753 Resort Hotel 0 185 2016 April
## 2754 Resort Hotel 0 185 2016 April
## 2755 Resort Hotel 0 185 2016 April
## 2756 Resort Hotel 0 185 2016 April
## 2757 Resort Hotel 0 185 2016 April
## 2758 Resort Hotel 0 185 2016 April
## 2759 Resort Hotel 0 185 2016 April
## 2760 Resort Hotel 0 185 2016 April
## 2761 Resort Hotel 0 185 2016 April
## 2762 Resort Hotel 0 185 2016 April
## 2763 Resort Hotel 0 185 2016 April
## 2764 Resort Hotel 0 172 2016 April
## 2765 Resort Hotel 0 172 2016 April
## 2766 Resort Hotel 0 172 2016 April
## 2767 Resort Hotel 0 172 2016 April
## 2768 Resort Hotel 0 172 2016 April
## 2769 Resort Hotel 0 172 2016 April
## 2770 Resort Hotel 0 215 2016 April
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## 2772 Resort Hotel 0 162 2016 April
## 2773 Resort Hotel 0 206 2016 April
## 2774 Resort Hotel 0 206 2016 April
## 2775 Resort Hotel 0 168 2016 April
## 2776 Resort Hotel 0 191 2016 April
## 2777 Resort Hotel 0 191 2016 April
## 2778 Resort Hotel 0 191 2016 April
## 2779 Resort Hotel 0 191 2016 April
## 2780 Resort Hotel 0 191 2016 April
## 2781 Resort Hotel 0 191 2016 April
## 2782 Resort Hotel 0 191 2016 April
## 2783 Resort Hotel 0 191 2016 April
## 2784 Resort Hotel 0 191 2016 April
## 2785 Resort Hotel 0 191 2016 April
## 2786 Resort Hotel 0 191 2016 April
## 2787 Resort Hotel 0 191 2016 April
## 2788 Resort Hotel 0 191 2016 April
## 2789 Resort Hotel 0 191 2016 April
## 2790 Resort Hotel 0 191 2016 April
## 2791 Resort Hotel 0 191 2016 April
## 2792 Resort Hotel 0 191 2016 April
## 2793 Resort Hotel 0 191 2016 April
## 2794 Resort Hotel 0 191 2016 April
## 2795 Resort Hotel 0 191 2016 April
## 2796 Resort Hotel 0 191 2016 April
## 2797 Resort Hotel 0 191 2016 April
## 2798 Resort Hotel 0 191 2016 April
## 2799 Resort Hotel 0 191 2016 April
## 2800 Resort Hotel 0 191 2016 April
## 2801 Resort Hotel 0 191 2016 April
## 2802 Resort Hotel 0 191 2016 April
## 2803 Resort Hotel 0 41 2016 April
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## 2808 Resort Hotel 0 7 2016 April
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## 2810 Resort Hotel 0 189 2016 April
## 2811 Resort Hotel 0 189 2016 April
## 2812 Resort Hotel 0 189 2016 April
## 2813 Resort Hotel 0 179 2016 April
## 2814 Resort Hotel 0 200 2016 April
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## 2816 Resort Hotel 0 197 2016 April
## 2817 Resort Hotel 0 197 2016 April
## 2818 Resort Hotel 0 197 2016 April
## 2819 Resort Hotel 0 197 2016 April
## 2820 Resort Hotel 0 197 2016 April
## 2821 Resort Hotel 0 197 2016 April
## 2822 Resort Hotel 0 197 2016 April
## 2823 Resort Hotel 0 171 2016 April
## 2824 Resort Hotel 0 173 2016 April
## 2825 Resort Hotel 0 206 2016 April
## 2826 Resort Hotel 0 255 2016 April
## 2827 Resort Hotel 0 171 2016 April
## 2828 Resort Hotel 0 229 2016 May
## 2829 Resort Hotel 0 199 2016 May
## 2830 Resort Hotel 0 199 2016 May
## 2831 Resort Hotel 0 199 2016 May
## 2832 Resort Hotel 0 199 2016 May
## 2833 Resort Hotel 0 199 2016 May
## 2834 Resort Hotel 0 199 2016 May
## 2835 Resort Hotel 0 199 2016 May
## 2836 Resort Hotel 0 199 2016 May
## 2837 Resort Hotel 0 199 2016 May
## 2838 Resort Hotel 0 199 2016 May
## 2839 Resort Hotel 0 199 2016 May
## 2840 Resort Hotel 0 199 2016 May
## 2841 Resort Hotel 0 199 2016 May
## 2842 Resort Hotel 0 199 2016 May
## 2843 Resort Hotel 0 199 2016 May
## 2844 Resort Hotel 0 199 2016 May
## 2845 Resort Hotel 0 199 2016 May
## 2846 Resort Hotel 0 199 2016 May
## 2847 Resort Hotel 0 199 2016 May
## 2848 Resort Hotel 0 199 2016 May
## 2849 Resort Hotel 0 199 2016 May
## 2850 Resort Hotel 0 199 2016 May
## 2851 Resort Hotel 0 199 2016 May
## 2852 Resort Hotel 0 199 2016 May
## 2853 Resort Hotel 0 199 2016 May
## 2854 Resort Hotel 0 199 2016 May
## 2855 Resort Hotel 0 215 2016 May
## 2856 Resort Hotel 0 203 2016 May
## 2857 Resort Hotel 0 201 2016 May
## 2858 Resort Hotel 0 226 2016 May
## 2859 Resort Hotel 0 188 2016 May
## 2860 Resort Hotel 0 288 2016 May
## 2861 Resort Hotel 0 188 2016 May
## 2862 Resort Hotel 0 288 2016 May
## 2863 Resort Hotel 0 188 2016 May
## 2864 Resort Hotel 0 209 2016 May
## 2865 Resort Hotel 0 189 2016 May
## 2866 Resort Hotel 0 209 2016 May
## 2867 Resort Hotel 0 209 2016 May
## 2868 Resort Hotel 0 253 2016 May
## 2869 Resort Hotel 0 253 2016 May
## 2870 Resort Hotel 0 252 2016 May
## 2871 Resort Hotel 0 354 2016 May
## 2872 Resort Hotel 0 260 2016 May
## 2873 Resort Hotel 0 179 2016 May
## 2874 Resort Hotel 0 262 2016 May
## 2875 Resort Hotel 0 232 2016 May
## 2876 Resort Hotel 0 232 2016 May
## 2877 Resort Hotel 0 236 2016 May
## 2878 Resort Hotel 0 256 2016 May
## 2879 Resort Hotel 0 212 2016 May
## 2880 Resort Hotel 0 202 2016 May
## 2881 Resort Hotel 0 235 2016 May
## 2882 Resort Hotel 0 203 2016 May
## 2883 Resort Hotel 0 203 2016 May
## 2884 Resort Hotel 0 203 2016 May
## 2885 Resort Hotel 0 203 2016 May
## 2886 Resort Hotel 0 203 2016 May
## 2887 Resort Hotel 0 203 2016 May
## 2888 Resort Hotel 0 29 2016 May
## 2889 Resort Hotel 0 188 2016 May
## 2890 Resort Hotel 0 236 2016 May
## 2891 Resort Hotel 0 236 2016 May
## 2892 Resort Hotel 0 236 2016 May
## 2893 Resort Hotel 0 236 2016 May
## 2894 Resort Hotel 0 236 2016 May
## 2895 Resort Hotel 0 236 2016 May
## 2896 Resort Hotel 0 236 2016 May
## 2897 Resort Hotel 0 236 2016 May
## 2898 Resort Hotel 0 236 2016 May
## 2899 Resort Hotel 0 236 2016 May
## 2900 Resort Hotel 0 236 2016 May
## 2901 Resort Hotel 0 236 2016 May
## 2902 Resort Hotel 0 236 2016 May
## 2903 Resort Hotel 0 189 2016 May
## 2904 Resort Hotel 0 189 2016 May
## 2905 Resort Hotel 0 213 2016 May
## 2906 Resort Hotel 0 213 2016 May
## 2907 Resort Hotel 0 237 2016 May
## 2908 Resort Hotel 0 237 2016 May
## 2909 Resort Hotel 0 195 2016 May
## 2910 Resort Hotel 0 195 2016 May
## 2911 Resort Hotel 0 206 2016 May
## 2912 Resort Hotel 0 211 2016 May
## 2913 Resort Hotel 0 211 2016 May
## 2914 Resort Hotel 0 211 2016 May
## 2915 Resort Hotel 0 211 2016 May
## 2916 Resort Hotel 0 211 2016 May
## 2917 Resort Hotel 0 211 2016 May
## 2918 Resort Hotel 0 211 2016 May
## 2919 Resort Hotel 0 211 2016 May
## 2920 Resort Hotel 0 211 2016 May
## 2921 Resort Hotel 0 211 2016 May
## 2922 Resort Hotel 0 211 2016 May
## 2923 Resort Hotel 0 211 2016 May
## 2924 Resort Hotel 0 211 2016 May
## 2925 Resort Hotel 0 211 2016 May
## 2926 Resort Hotel 0 211 2016 May
## 2927 Resort Hotel 0 211 2016 May
## 2928 Resort Hotel 0 211 2016 May
## 2929 Resort Hotel 0 211 2016 May
## 2930 Resort Hotel 0 211 2016 May
## 2931 Resort Hotel 0 211 2016 May
## 2932 Resort Hotel 0 211 2016 May
## 2933 Resort Hotel 0 211 2016 May
## 2934 Resort Hotel 0 211 2016 May
## 2935 Resort Hotel 0 211 2016 May
## 2936 Resort Hotel 0 211 2016 May
## 2937 Resort Hotel 0 211 2016 May
## 2938 Resort Hotel 0 211 2016 May
## 2939 Resort Hotel 0 211 2016 May
## 2940 Resort Hotel 0 211 2016 May
## 2941 Resort Hotel 0 9 2016 May
## 2942 Resort Hotel 0 239 2016 May
## 2943 Resort Hotel 0 205 2016 May
## 2944 Resort Hotel 0 208 2016 May
## 2945 Resort Hotel 0 213 2016 May
## 2946 Resort Hotel 0 213 2016 May
## 2947 Resort Hotel 0 213 2016 May
## 2948 Resort Hotel 0 213 2016 May
## 2949 Resort Hotel 0 213 2016 May
## 2950 Resort Hotel 0 213 2016 May
## 2951 Resort Hotel 0 213 2016 May
## 2952 Resort Hotel 0 213 2016 May
## 2953 Resort Hotel 0 213 2016 May
## 2954 Resort Hotel 0 213 2016 May
## 2955 Resort Hotel 0 213 2016 May
## 2956 Resort Hotel 0 213 2016 May
## 2957 Resort Hotel 0 213 2016 May
## 2958 Resort Hotel 0 213 2016 May
## 2959 Resort Hotel 0 213 2016 May
## 2960 Resort Hotel 0 213 2016 May
## 2961 Resort Hotel 0 213 2016 May
## 2962 Resort Hotel 0 213 2016 May
## 2963 Resort Hotel 0 213 2016 May
## 2964 Resort Hotel 0 213 2016 May
## 2965 Resort Hotel 0 213 2016 May
## 2966 Resort Hotel 0 213 2016 May
## 2967 Resort Hotel 0 213 2016 May
## 2968 Resort Hotel 0 213 2016 May
## 2969 Resort Hotel 0 213 2016 May
## 2970 Resort Hotel 0 213 2016 May
## 2971 Resort Hotel 0 213 2016 May
## 2972 Resort Hotel 0 213 2016 May
## 2973 Resort Hotel 0 213 2016 May
## 2974 Resort Hotel 0 213 2016 May
## 2975 Resort Hotel 0 201 2016 May
## 2976 Resort Hotel 0 201 2016 May
## 2977 Resort Hotel 0 189 2016 May
## 2978 Resort Hotel 0 265 2016 May
## 2979 Resort Hotel 0 215 2016 May
## 2980 Resort Hotel 0 265 2016 May
## 2981 Resort Hotel 0 201 2016 May
## 2982 Resort Hotel 0 189 2016 May
## 2983 Resort Hotel 0 201 2016 May
## 2984 Resort Hotel 0 201 2016 May
## 2985 Resort Hotel 0 201 2016 May
## 2986 Resort Hotel 0 196 2016 May
## 2987 Resort Hotel 0 253 2016 May
## 2988 Resort Hotel 0 224 2016 May
## 2989 Resort Hotel 0 224 2016 May
## 2990 Resort Hotel 0 139 2016 May
## 2991 Resort Hotel 0 239 2016 May
## 2992 Resort Hotel 0 191 2016 May
## 2993 Resort Hotel 0 3 2016 May
## 2994 Resort Hotel 0 304 2016 May
## 2995 Resort Hotel 0 304 2016 May
## 2996 Resort Hotel 0 274 2016 May
## 2997 Resort Hotel 0 237 2016 May
## 2998 Resort Hotel 0 193 2016 May
## 2999 Resort Hotel 0 217 2016 May
## 3000 Resort Hotel 0 5 2016 May
## 3001 Resort Hotel 0 210 2016 May
## 3002 Resort Hotel 0 192 2016 May
## 3003 Resort Hotel 0 220 2016 May
## 3004 Resort Hotel 0 248 2016 May
## 3005 Resort Hotel 0 236 2016 May
## 3006 Resort Hotel 0 212 2016 May
## 3007 Resort Hotel 0 307 2016 May
## 3008 Resort Hotel 0 221 2016 May
## 3009 Resort Hotel 0 233 2016 May
## 3010 Resort Hotel 0 222 2016 May
## 3011 Resort Hotel 0 257 2016 May
## 3012 Resort Hotel 0 206 2016 May
## 3013 Resort Hotel 0 222 2016 May
## 3014 Resort Hotel 0 223 2016 May
## 3015 Resort Hotel 0 223 2016 May
## 3016 Resort Hotel 0 223 2016 May
## 3017 Resort Hotel 0 223 2016 May
## 3018 Resort Hotel 0 223 2016 May
## 3019 Resort Hotel 0 223 2016 May
## 3020 Resort Hotel 0 223 2016 May
## 3021 Resort Hotel 0 223 2016 May
## 3022 Resort Hotel 0 223 2016 May
## 3023 Resort Hotel 0 223 2016 May
## 3024 Resort Hotel 0 223 2016 May
## 3025 Resort Hotel 0 223 2016 May
## 3026 Resort Hotel 0 223 2016 May
## 3027 Resort Hotel 0 223 2016 May
## 3028 Resort Hotel 0 223 2016 May
## 3029 Resort Hotel 0 223 2016 May
## 3030 Resort Hotel 0 223 2016 May
## 3031 Resort Hotel 0 223 2016 May
## 3032 Resort Hotel 0 223 2016 May
## 3033 Resort Hotel 0 213 2016 May
## 3034 Resort Hotel 0 223 2016 May
## 3035 Resort Hotel 0 223 2016 May
## 3036 Resort Hotel 0 227 2016 May
## 3037 Resort Hotel 0 227 2016 May
## 3038 Resort Hotel 0 257 2016 May
## 3039 Resort Hotel 0 224 2016 May
## 3040 Resort Hotel 0 224 2016 May
## 3041 Resort Hotel 0 224 2016 May
## 3042 Resort Hotel 0 224 2016 May
## 3043 Resort Hotel 0 224 2016 May
## 3044 Resort Hotel 0 224 2016 May
## 3045 Resort Hotel 0 224 2016 May
## 3046 Resort Hotel 0 224 2016 May
## 3047 Resort Hotel 0 224 2016 May
## 3048 Resort Hotel 0 224 2016 May
## 3049 Resort Hotel 0 197 2016 May
## 3050 Resort Hotel 0 225 2016 May
## 3051 Resort Hotel 0 225 2016 May
## 3052 Resort Hotel 0 225 2016 May
## 3053 Resort Hotel 0 225 2016 May
## 3054 Resort Hotel 0 225 2016 May
## 3055 Resort Hotel 0 225 2016 May
## 3056 Resort Hotel 0 225 2016 May
## 3057 Resort Hotel 0 225 2016 May
## 3058 Resort Hotel 0 225 2016 May
## 3059 Resort Hotel 0 225 2016 May
## 3060 Resort Hotel 0 225 2016 May
## 3061 Resort Hotel 0 225 2016 May
## 3062 Resort Hotel 0 225 2016 May
## 3063 Resort Hotel 0 225 2016 May
## 3064 Resort Hotel 0 225 2016 May
## 3065 Resort Hotel 0 225 2016 May
## 3066 Resort Hotel 0 225 2016 May
## 3067 Resort Hotel 0 225 2016 May
## 3068 Resort Hotel 0 225 2016 May
## 3069 Resort Hotel 0 225 2016 May
## 3070 Resort Hotel 0 225 2016 May
## 3071 Resort Hotel 0 225 2016 May
## 3072 Resort Hotel 0 225 2016 May
## 3073 Resort Hotel 0 225 2016 May
## 3074 Resort Hotel 0 225 2016 May
## 3075 Resort Hotel 0 225 2016 May
## 3076 Resort Hotel 0 225 2016 May
## 3077 Resort Hotel 0 225 2016 May
## 3078 Resort Hotel 0 225 2016 May
## 3079 Resort Hotel 0 225 2016 May
## 3080 Resort Hotel 0 225 2016 May
## 3081 Resort Hotel 0 225 2016 May
## 3082 Resort Hotel 0 225 2016 May
## 3083 Resort Hotel 0 225 2016 May
## 3084 Resort Hotel 0 225 2016 May
## 3085 Resort Hotel 0 225 2016 May
## 3086 Resort Hotel 0 225 2016 May
## 3087 Resort Hotel 0 264 2016 May
## 3088 Resort Hotel 0 311 2016 May
## 3089 Resort Hotel 0 238 2016 May
## 3090 Resort Hotel 0 264 2016 May
## 3091 Resort Hotel 0 226 2016 May
## 3092 Resort Hotel 0 204 2016 May
## 3093 Resort Hotel 0 312 2016 May
## 3094 Resort Hotel 0 204 2016 May
## 3095 Resort Hotel 0 204 2016 May
## 3096 Resort Hotel 0 312 2016 May
## 3097 Resort Hotel 0 225 2016 May
## 3098 Resort Hotel 0 225 2016 May
## 3099 Resort Hotel 0 225 2016 May
## 3100 Resort Hotel 0 225 2016 May
## 3101 Resort Hotel 0 225 2016 May
## 3102 Resort Hotel 0 225 2016 May
## 3103 Resort Hotel 0 225 2016 May
## 3104 Resort Hotel 0 225 2016 May
## 3105 Resort Hotel 0 225 2016 May
## 3106 Resort Hotel 0 225 2016 May
## 3107 Resort Hotel 0 225 2016 May
## 3108 Resort Hotel 0 225 2016 May
## 3109 Resort Hotel 0 225 2016 May
## 3110 Resort Hotel 0 225 2016 May
## 3111 Resort Hotel 0 225 2016 May
## 3112 Resort Hotel 0 213 2016 May
## 3113 Resort Hotel 0 225 2016 May
## 3114 Resort Hotel 0 225 2016 May
## 3115 Resort Hotel 0 225 2016 May
## 3116 Resort Hotel 0 210 2016 May
## 3117 Resort Hotel 0 314 2016 May
## 3118 Resort Hotel 0 0 2016 May
## 3119 Resort Hotel 0 0 2016 May
## 3120 Resort Hotel 0 229 2016 May
## 3121 Resort Hotel 0 215 2016 May
## 3122 Resort Hotel 0 215 2016 May
## 3123 Resort Hotel 0 215 2016 May
## 3124 Resort Hotel 0 213 2016 May
## arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights
## 1 1 0 0
## 2 1 0 0
## 3 1 0 1
## 4 1 0 1
## 5 1 0 2
## 6 1 0 2
## 7 1 0 2
## 8 1 0 2
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## 13 1 0 4
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## 29 2 2 4
## 30 2 1 3
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## 38 2 2 5
## 39 2 2 5
## 40 2 2 5
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## 190 9 2 5
## 191 9 2 5
## 192 9 2 5
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## 197 10 0 2
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## 1401 2 none 0 BB GBR Online TA
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## 1403 2 none 0 BB ESP Online TA
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## 1414 2 none 0 BB MEX Online TA
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## 1420 2 none 0 HB POL Online TA
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## 1423 2 none 0 BB DEU Online TA
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## 1425 1 none 0 BB IRL Direct
## 1426 3 none 0 BB AGO Complementary
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## 1428 2 none 0 BB PRT Online TA
## 1429 2 none 0 BB PRT Offline TA/TO
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## 1431 2 none 0 BB BEL Offline TA/TO
## 1432 2 none 0 BB GBR Offline TA/TO
## 1433 2 none 0 BB GBR Offline TA/TO
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## 1436 1 none 0 HB DEU Offline TA/TO
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## 1444 2 none 0 BB PRT Direct
## 1445 2 none 0 BB FRA Online TA
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## 1448 1 none 0 BB PRT Direct
## 1449 2 none 0 BB PRT Corporate
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## 1453 2 none 0 BB PRT Corporate
## 1454 2 none 0 BB PRT Direct
## 1455 2 none 0 BB PRT Corporate
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## 1457 2 none 0 BB DEU Online TA
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## 1461 2 none 0 BB GBR Offline TA/TO
## 1462 2 none 0 BB IRL Direct
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## 1467 2 children 1 BB PRT Direct
## 1468 1 none 0 BB PRT Direct
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## 1472 2 none 0 BB ESP Online TA
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## 1477 2 none 0 BB ESP Online TA
## 1478 2 none 0 HB ESP Offline TA/TO
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## 1481 2 none 0 BB DEU Online TA
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## 1485 2 none 0 BB ESP Offline TA/TO
## 1486 2 none 0 BB PRT Online TA
## 1487 2 none 0 BB ISR Direct
## 1488 2 none 0 BB PRT Online TA
## 1489 2 none 0 BB ESP Online TA
## 1490 2 none 0 BB GBR Online TA
## 1491 2 none 0 BB ESP Offline TA/TO
## 1492 2 none 0 HB LTU Offline TA/TO
## 1493 2 none 0 BB PRT Corporate
## 1494 2 none 0 BB PRT Corporate
## 1495 2 none 0 BB ESP Online TA
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## 1497 2 none 0 HB ESP Direct
## 1498 2 none 0 HB ESP Online TA
## 1499 2 children 0 BB ESP Online TA
## 1500 2 none 0 HB ESP Online TA
## 1501 2 children 0 BB CHL Online TA
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## 1506 2 none 0 HB ESP Direct
## 1507 2 none 0 HB ESP Online TA
## 1508 2 none 0 BB ESP Online TA
## 1509 2 none 0 BB ESP Online TA
## 1510 2 none 0 BB ESP Direct
## 1511 2 none 0 BB GBR Direct
## 1512 2 none 0 HB BEL Direct
## 1513 2 none 0 BB ESP Online TA
## 1514 2 none 0 BB ESP Online TA
## 1515 2 none 0 BB ESP Online TA
## 1516 2 none 0 BB PRT Offline TA/TO
## 1517 2 none 0 BB ESP Online TA
## 1518 2 none 0 BB PRT Direct
## 1519 2 none 0 BB PRT Online TA
## 1520 2 none 0 BB PRT Online TA
## 1521 1 none 0 BB PRT Offline TA/TO
## 1522 2 none 0 BB ESP Online TA
## 1523 2 none 0 BB ESP Online TA
## 1524 2 none 0 BB GBR Online TA
## 1525 2 none 0 BB RUS Online TA
## 1526 2 none 0 BB GBR Offline TA/TO
## 1527 1 none 0 BB GBR Online TA
## 1528 2 none 0 HB PRT Online TA
## 1529 1 none 0 SC PRT Corporate
## 1530 2 none 0 BB PRT Online TA
## 1531 2 none 0 BB PRT Corporate
## 1532 2 none 0 BB PRT Corporate
## 1533 2 none 0 BB PRT Corporate
## 1534 1 none 0 BB PRT Offline TA/TO
## 1535 2 none 0 BB ESP Online TA
## 1536 1 none 0 BB PRT Corporate
## 1537 2 none 0 BB GBR Online TA
## 1538 2 none 0 BB FRA Groups
## 1539 2 children 0 BB GBR Offline TA/TO
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## 1545 1 none 0 BB PRT Online TA
## 1546 2 children 0 BB IRL Online TA
## 1547 2 none 0 BB GBR Direct
## 1548 2 none 0 HB FRA Direct
## 1549 1 none 0 BB PRT Offline TA/TO
## 1550 1 none 0 BB NLD Online TA
## 1551 2 none 0 BB PRT Online TA
## 1552 1 none 0 BB ESP Offline TA/TO
## 1553 2 none 0 BB ESP Online TA
## 1554 1 none 0 BB NLD Online TA
## 1555 2 none 0 BB GBR Online TA
## 1556 2 none 0 BB GBR Groups
## 1557 1 none 0 BB GBR Groups
## 1558 2 children 0 BB GBR Direct
## 1559 2 none 0 BB GBR Offline TA/TO
## 1560 2 none 0 BB PRT Online TA
## 1561 2 none 0 BB PRT Online TA
## 1562 2 none 0 BB PRT Online TA
## 1563 2 none 0 BB DEU Direct
## 1564 2 none 0 BB PRT Groups
## 1565 1 none 0 HB PRT Complementary
## 1566 2 none 0 BB GBR Groups
## 1567 2 none 0 BB USA Online TA
## 1568 1 none 0 BB GBR Online TA
## 1569 2 none 0 BB GBR Online TA
## 1570 2 none 0 BB PRT Online TA
## 1571 2 none 0 BB GBR Offline TA/TO
## 1572 2 none 0 BB PRT Direct
## 1573 2 none 0 BB GBR Online TA
## 1574 2 none 0 BB PRT Online TA
## 1575 2 none 0 HB GBR Direct
## 1576 2 none 0 BB GBR Offline TA/TO
## 1577 2 children 0 BB GBR Online TA
## 1578 2 children 0 BB GBR Online TA
## 1579 2 none 0 BB GBR Offline TA/TO
## 1580 2 none 0 BB GBR Offline TA/TO
## 1581 2 none 0 BB IRL Online TA
## 1582 2 none 0 BB IRL Online TA
## 1583 1 none 0 BB PRT Corporate
## 1584 2 none 0 BB ESP Online TA
## 1585 2 none 0 BB PRT Offline TA/TO
## 1586 2 none 0 BB PRT Offline TA/TO
## 1587 2 none 0 BB PRT Direct
## 1588 1 none 0 BB PRT Offline TA/TO
## 1589 2 none 0 HB ESP Online TA
## 1590 2 none 0 BB ESP Online TA
## 1591 2 none 0 HB ESP Online TA
## 1592 2 none 0 BB GBR Online TA
## 1593 2 none 0 HB ESP Groups
## 1594 2 none 0 HB ESP Groups
## 1595 2 none 0 HB ESP Groups
## 1596 1 none 0 HB ESP Groups
## 1597 2 none 0 HB ESP Groups
## 1598 2 none 0 HB ESP Groups
## 1599 2 none 0 HB ESP Groups
## 1600 2 none 0 HB ESP Groups
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## 1604 2 none 0 HB ESP Groups
## 1605 2 none 0 HB ESP Groups
## 1606 2 none 0 HB ESP Groups
## 1607 2 none 0 HB ESP Groups
## 1608 2 none 0 HB ESP Groups
## 1609 2 none 0 HB ESP Groups
## 1610 2 none 0 HB ESP Groups
## 1611 2 none 0 BB IRL Online TA
## 1612 2 none 0 HB ESP Groups
## 1613 2 none 0 HB ESP Groups
## 1614 2 none 0 HB PRT Groups
## 1615 1 none 0 HB ESP Groups
## 1616 2 none 0 HB ESP Groups
## 1617 2 none 0 HB GBR Offline TA/TO
## 1618 2 none 0 BB GBR Offline TA/TO
## 1619 2 none 0 BB ESP Online TA
## 1620 2 none 0 HB GBR Offline TA/TO
## 1621 2 none 0 HB GBR Offline TA/TO
## 1622 2 none 0 BB ESP Direct
## 1623 2 none 0 BB ESP Direct
## 1624 2 none 0 BB PRT Direct
## 1625 2 none 0 BB FRA Direct
## 1626 1 none 0 BB ESP Online TA
## 1627 3 none 0 BB GBR Offline TA/TO
## 1628 3 none 0 BB IRL Offline TA/TO
## 1629 3 none 0 HB GBR Offline TA/TO
## 1630 2 none 0 BB GBR Offline TA/TO
## 1631 2 none 0 BB GBR Offline TA/TO
## 1632 1 none 0 BB DEU Online TA
## 1633 1 none 0 BB PRT Online TA
## 1634 1 none 0 BB PRT Online TA
## 1635 2 none 0 HB FRA Offline TA/TO
## 1636 2 none 0 HB FRA Offline TA/TO
## 1637 2 none 0 BB PRT Online TA
## 1638 2 none 0 BB IND Online TA
## 1639 2 none 0 BB PRT Online TA
## 1640 2 none 0 BB GBR Offline TA/TO
## 1641 2 none 0 BB ESP Online TA
## 1642 2 none 0 BB LUX Direct
## 1643 2 none 0 HB FRA Online TA
## 1644 2 none 0 BB GBR Online TA
## 1645 1 none 0 BB FIN Direct
## 1646 2 none 0 HB GBR Online TA
## 1647 2 none 0 BB FRA Online TA
## 1648 2 none 0 BB CYM Online TA
## 1649 2 none 0 BB GBR Online TA
## 1650 2 none 0 BB GBR Offline TA/TO
## 1651 2 none 0 BB BEL Online TA
## 1652 2 none 0 HB GBR Offline TA/TO
## 1653 2 none 0 BB GBR Offline TA/TO
## 1654 2 none 0 BB GBR Offline TA/TO
## 1655 2 none 0 BB GBR Offline TA/TO
## 1656 2 none 0 BB GBR Offline TA/TO
## 1657 2 none 0 BB ESP Online TA
## 1658 1 none 0 BB GBR Online TA
## 1659 2 none 0 BB GBR Online TA
## 1660 2 none 0 BB ESP Online TA
## 1661 1 none 0 BB ESP Online TA
## 1662 2 none 0 BB ESP Online TA
## 1663 2 none 0 BB ESP Online TA
## 1664 2 none 0 BB FRA Online TA
## 1665 1 none 0 BB BEL Online TA
## 1666 2 none 0 BB GBR Offline TA/TO
## 1667 2 none 0 BB GBR Offline TA/TO
## 1668 2 none 0 HB GBR Offline TA/TO
## 1669 2 children 0 BB ESP Online TA
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## 1671 2 none 0 BB BEL Online TA
## 1672 2 none 0 BB BEL Online TA
## 1673 2 none 0 BB BEL Online TA
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## 1675 2 none 0 BB PRT Online TA
## 1676 2 none 0 BB ESP Online TA
## 1677 2 none 0 BB PRT Direct
## 1678 2 none 0 BB PRT Online TA
## 1679 2 none 0 BB PRT Direct
## 1680 2 none 0 BB PRT Direct
## 1681 1 none 0 BB ESP Complementary
## 1682 2 none 0 BB ESP Direct
## 1683 2 none 0 BB PRT Direct
## 1684 2 none 0 BB PRT Online TA
## 1685 2 none 0 BB PRT Direct
## 1686 2 none 0 BB PRT Direct
## 1687 2 none 0 BB GBR Online TA
## 1688 2 none 0 BB IRL Online TA
## 1689 2 none 0 BB GBR Online TA
## 1690 2 none 0 BB GBR Offline TA/TO
## 1691 2 none 0 BB GBR Offline TA/TO
## 1692 2 none 0 BB IRL Online TA
## 1693 2 none 0 HB GBR Direct
## 1694 2 none 0 BB ESP Online TA
## 1695 3 none 0 HB GBR Offline TA/TO
## 1696 2 none 0 BB GBR Offline TA/TO
## 1697 1 none 0 BB FIN Online TA
## 1698 2 none 0 BB PRT Online TA
## 1699 2 none 0 HB ESP Direct
## 1700 1 none 0 BB GBR Online TA
## 1701 2 none 0 BB PRT Direct
## 1702 2 none 0 BB ESP Online TA
## 1703 1 none 0 BB PRT Direct
## 1704 2 none 0 BB GBR Online TA
## 1705 2 none 0 BB PRT Online TA
## 1706 2 none 0 BB GBR Offline TA/TO
## 1707 2 none 0 BB GBR Offline TA/TO
## 1708 2 none 0 BB IRL Offline TA/TO
## 1709 2 children 0 BB ZMB Online TA
## 1710 2 none 0 BB GBR Offline TA/TO
## 1711 2 none 0 BB IRL Offline TA/TO
## 1712 2 none 0 BB CHE Online TA
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## 1714 2 none 0 BB PRT Online TA
## 1715 2 none 0 BB IRL Offline TA/TO
## 1716 1 children 0 BB GBR Offline TA/TO
## 1717 2 none 0 HB GBR Offline TA/TO
## 1718 2 none 0 BB DEU Online TA
## 1719 2 children 0 BB IRL Online TA
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## 1721 2 none 0 BB PRT Online TA
## 1722 2 none 0 BB IRL Online TA
## 1723 1 none 0 BB AUT Corporate
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## 1726 2 none 0 BB NLD Direct
## 1727 2 none 0 BB PRT Online TA
## 1728 1 none 0 BB PRT Direct
## 1729 2 none 0 BB AUT Online TA
## 1730 2 none 0 HB ESP Offline TA/TO
## 1731 2 none 0 BB AUS Online TA
## 1732 2 none 0 BB ESP Online TA
## 1733 2 none 0 HB ESP Offline TA/TO
## 1734 1 none 0 BB PRT Online TA
## 1735 2 none 0 BB IRL Online TA
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## 1737 2 none 0 HB FIN Online TA
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## 1749 2 none 0 BB PRT Direct
## 1750 1 none 0 BB PRT Online TA
## 1751 1 none 0 BB USA Online TA
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## 1753 2 none 0 BB PRT Online TA
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## 1755 2 none 0 BB PRT Groups
## 1756 2 none 0 BB PRT Online TA
## 1757 2 none 0 BB PRT Groups
## 1758 2 none 0 BB PRT Groups
## 1759 2 none 0 BB PRT Groups
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## 1762 2 none 0 BB PRT Direct
## 1763 2 none 0 BB PRT Direct
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## 1765 2 none 0 BB PRT Groups
## 1766 2 none 0 BB USA Online TA
## 1767 2 none 0 BB PRT Groups
## 1768 2 none 0 BB PRT Groups
## 1769 2 children 1 BB PRT Online TA
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## 1774 2 none 0 BB ESP Online TA
## 1775 2 none 0 BB ESP Offline TA/TO
## 1776 2 none 0 BB ESP Offline TA/TO
## 1777 2 none 0 BB PRT Groups
## 1778 1 none 0 BB PRT Online TA
## 1779 2 none 0 BB CN Offline TA/TO
## 1780 2 none 0 BB ESP Offline TA/TO
## 1781 2 none 0 BB CPV Direct
## 1782 2 none 0 BB BEL Online TA
## 1783 2 none 0 BB GBR Online TA
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## 1785 2 none 0 BB PRT Online TA
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## 1787 2 none 0 BB CHE Groups
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## 1789 1 none 0 BB PRT Groups
## 1790 2 none 0 BB PRT Offline TA/TO
## 1791 2 none 0 BB ESP Direct
## 1792 2 none 0 BB PRT Groups
## 1793 2 none 0 BB PRT Online TA
## 1794 2 children 0 BB PRT Online TA
## 1795 2 children 0 BB ESP Direct
## 1796 2 none 0 BB PRT Groups
## 1797 2 none 0 BB PRT Groups
## 1798 2 none 0 BB PRT Groups
## 1799 2 none 0 BB PRT Offline TA/TO
## 1800 1 none 0 BB PRT Groups
## 1801 1 none 0 BB PRT Offline TA/TO
## 1802 2 none 0 BB USA Online TA
## 1803 1 none 0 BB PRT Groups
## 1804 2 none 0 BB PRT Offline TA/TO
## 1805 2 none 0 BB PRT Groups
## 1806 2 none 0 BB PRT Groups
## 1807 2 none 0 BB PRT Groups
## 1808 2 none 0 BB PRT Online TA
## 1809 2 none 0 BB PRT Groups
## 1810 1 none 0 BB PRT Groups
## 1811 2 none 0 BB PRT Groups
## 1812 2 none 0 HB PRT Direct
## 1813 3 children 0 BB GBR Online TA
## 1814 1 none 0 BB PRT Groups
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## 1817 2 none 0 BB PRT Groups
## 1818 2 none 0 BB FRA Online TA
## 1819 2 none 0 BB PRT Online TA
## 1820 2 none 0 BB GBR Offline TA/TO
## 1821 2 none 0 BB GBR Offline TA/TO
## 1822 1 none 0 BB PRT Online TA
## 1823 2 none 0 HB ESP Offline TA/TO
## 1824 2 none 0 BB ESP Online TA
## 1825 2 none 0 HB GBR Offline TA/TO
## 1826 2 none 0 BB GBR Offline TA/TO
## 1827 2 none 0 HB GBR Offline TA/TO
## 1828 2 none 0 BB IRL Offline TA/TO
## 1829 2 none 0 BB ESP Online TA
## 1830 2 none 0 BB DEU Direct
## 1831 2 none 0 BB GBR Online TA
## 1832 2 children 1 BB POL Offline TA/TO
## 1833 2 children 1 BB POL Offline TA/TO
## 1834 2 none 0 BB FIN Online TA
## 1835 2 none 0 BB PRT Online TA
## 1836 1 none 0 BB FRA Corporate
## 1837 2 none 0 BB FRA Corporate
## 1838 1 none 0 BB PRT Online TA
## 1839 1 none 0 BB PRT Direct
## 1840 2 none 0 BB PRT Direct
## 1841 1 none 0 BB PRT Online TA
## 1842 2 none 0 BB ESP Corporate
## 1843 2 none 0 BB PRT Online TA
## 1844 2 none 0 BB PRT Offline TA/TO
## 1845 2 children 0 BB BEL Offline TA/TO
## 1846 1 none 0 BB PRT Corporate
## 1847 1 none 0 BB BEL Offline TA/TO
## 1848 3 none 0 BB NLD Offline TA/TO
## 1849 2 none 0 BB ESP Online TA
## 1850 1 none 0 BB PRT Corporate
## 1851 2 none 0 BB ITA Corporate
## 1852 2 none 0 BB ITA Corporate
## 1853 2 none 0 BB ESP Corporate
## 1854 1 none 0 BB ITA Corporate
## 1855 2 none 0 BB ITA Corporate
## 1856 1 none 0 BB FRA Corporate
## 1857 1 none 0 BB FRA Corporate
## 1858 1 none 0 BB PRT Corporate
## 1859 2 none 0 BB PRT Online TA
## 1860 1 none 0 BB PRT Online TA
## 1861 1 none 0 BB PRT Online TA
## 1862 1 none 0 BB PRT Corporate
## 1863 1 none 0 BB PRT Corporate
## 1864 1 none 0 BB PRT Corporate
## 1865 1 none 0 BB PRT Corporate
## 1866 2 none 0 HB ESP Online TA
## 1867 2 none 0 HB ESP Online TA
## 1868 1 none 0 BB PRT Corporate
## 1869 2 none 0 BB GBR Offline TA/TO
## 1870 2 none 0 BB PRT Corporate
## 1871 1 none 0 BB USA Online TA
## 1872 1 none 0 BB PRT Online TA
## 1873 1 none 0 HB ESP Online TA
## 1874 2 children 0 HB PRT Direct
## 1875 2 none 0 BB ZWE Offline TA/TO
## 1876 2 none 0 BB ESP Online TA
## 1877 2 none 0 BB ESP Online TA
## 1878 2 children 0 HB GBR Online TA
## 1879 2 none 0 BB GBR Offline TA/TO
## 1880 1 none 0 BB FRA Corporate
## 1881 1 none 0 BB AUT Corporate
## 1882 1 none 0 BB AUT Corporate
## 1883 1 none 0 BB AUT Corporate
## 1884 1 none 0 BB AUT Corporate
## 1885 1 none 0 BB FRA Corporate
## 1886 1 none 0 BB AUT Corporate
## 1887 2 none 0 BB PRT Online TA
## 1888 2 none 0 BB PRT Online TA
## 1889 2 children 1 BB PRT Offline TA/TO
## 1890 2 none 0 BB ESP Direct
## 1891 2 none 0 BB PRT Online TA
## 1892 2 none 0 HB ESP Online TA
## 1893 2 none 0 HB ESP Online TA
## 1894 1 none 0 BB DZA Offline TA/TO
## 1895 1 none 0 BB DZA Offline TA/TO
## 1896 2 none 0 BB FIN Offline TA/TO
## 1897 2 none 0 BB IRL Offline TA/TO
## 1898 1 none 0 BB DEU Corporate
## 1899 2 none 0 BB RUS Online TA
## 1900 1 none 0 BB DEU Corporate
## 1901 1 none 0 BB BEL Corporate
## 1902 1 none 0 BB CZE Corporate
## 1903 1 none 0 BB GBR Corporate
## 1904 1 none 0 BB DEU Corporate
## 1905 1 none 0 BB DEU Corporate
## 1906 1 none 0 BB AUT Corporate
## 1907 2 none 0 BB GBR Online TA
## 1908 2 none 0 HB ESP Online TA
## 1909 2 none 0 BB ESP Direct
## 1910 1 none 0 BB GBR Online TA
## 1911 2 none 0 BB FRA Corporate
## 1912 2 none 0 BB PRT Corporate
## 1913 1 none 0 BB PRT Corporate
## 1914 2 none 0 BB FRA Corporate
## 1915 2 none 0 BB BRA Online TA
## 1916 1 none 0 BB PRT Corporate
## 1917 2 none 0 BB PRT Corporate
## 1918 2 none 0 BB PRT Offline TA/TO
## 1919 1 none 0 BB PRT Online TA
## 1920 1 none 0 BB PRT Online TA
## 1921 1 none 0 BB ESP Direct
## 1922 3 children 0 BB PRT Online TA
## 1923 2 none 0 BB PRT Direct
## 1924 2 none 0 HB ESP Online TA
## 1925 1 none 0 BB PRT Corporate
## 1926 2 none 0 HB ESP Online TA
## 1927 2 none 0 BB PRT Offline TA/TO
## 1928 2 none 0 BB PRT Offline TA/TO
## 1929 2 none 0 BB FRA Online TA
## 1930 2 none 0 HB FRA Online TA
## 1931 2 none 0 HB GBR Offline TA/TO
## 1932 1 none 0 BB PRT Online TA
## 1933 2 children 0 BB ESP Direct
## 1934 2 none 0 BB PRT Direct
## 1935 1 none 0 BB PRT Offline TA/TO
## 1936 1 none 0 BB ESP Corporate
## 1937 1 none 0 BB GBR Online TA
## 1938 1 none 0 BB DEU Online TA
## 1939 2 none 0 BB CHE Online TA
## 1940 1 none 0 BB GBR Online TA
## 1941 1 none 0 BB GBR Online TA
## 1942 2 none 0 BB PRT Corporate
## 1943 1 none 0 BB PRT Corporate
## 1944 1 none 0 BB GBR Online TA
## 1945 2 none 0 BB ESP Corporate
## 1946 1 none 0 BB DEU Online TA
## 1947 1 none 0 BB GBR Online TA
## 1948 1 none 0 BB GBR Online TA
## 1949 2 none 0 BB DEU Online TA
## 1950 2 none 0 BB DEU Online TA
## 1951 1 none 0 BB DEU Corporate
## 1952 2 none 0 BB SWE Online TA
## 1953 1 none 0 BB PRT Corporate
## 1954 2 none 0 BB AUT Corporate
## 1955 1 none 0 BB FRA Corporate
## 1956 2 none 0 BB ITA Online TA
## 1957 1 none 0 BB FRA Corporate
## 1958 1 none 0 BB FRA Corporate
## 1959 1 none 0 BB FRA Corporate
## 1960 1 none 0 BB PRT Corporate
## 1961 1 none 0 BB FRA Corporate
## 1962 2 none 0 BB GBR Online TA
## 1963 2 none 0 BB RUS Online TA
## 1964 1 none 0 BB FRA Corporate
## 1965 2 children 0 BB RUS Online TA
## 1966 2 none 0 BB RUS Online TA
## 1967 2 none 0 BB NLD Corporate
## 1968 1 none 0 BB FRA Corporate
## 1969 2 none 0 BB CHE Online TA
## 1970 1 none 0 BB ESP Corporate
## 1971 2 none 0 BB PRT Online TA
## 1972 1 none 0 BB PRT Online TA
## 1973 2 none 0 BB PRT Direct
## 1974 2 none 0 BB ITA Online TA
## 1975 2 none 0 BB PRT Direct
## 1976 2 none 0 BB PRT Online TA
## 1977 1 none 0 BB PRT Direct
## 1978 2 none 0 BB ITA Corporate
## 1979 2 none 0 BB GBR Online TA
## 1980 2 none 0 BB GBR Online TA
## 1981 2 none 0 BB GBR Online TA
## 1982 2 none 0 HB GBR Offline TA/TO
## 1983 2 none 0 BB PRT Online TA
## 1984 2 none 0 BB PRT Online TA
## 1985 2 children 0 BB PRT Offline TA/TO
## 1986 2 children 1 BB PRT Offline TA/TO
## 1987 2 none 0 BB FRA Online TA
## 1988 2 none 0 BB GBR Offline TA/TO
## 1989 1 none 0 BB PRT Online TA
## 1990 1 none 0 BB NOR Online TA
## 1991 2 none 0 Undefined ESP Offline TA/TO
## 1992 2 none 0 Undefined ESP Offline TA/TO
## 1993 2 none 0 Undefined ESP Offline TA/TO
## 1994 2 none 0 Undefined ESP Offline TA/TO
## 1995 2 none 0 Undefined ESP Offline TA/TO
## 1996 2 none 0 Undefined ESP Offline TA/TO
## 1997 2 none 0 Undefined ESP Offline TA/TO
## 1998 1 none 0 Undefined ESP Offline TA/TO
## 1999 2 none 0 Undefined ESP Offline TA/TO
## 2000 2 none 0 Undefined ESP Offline TA/TO
## 2001 2 none 0 Undefined ESP Offline TA/TO
## 2002 2 none 0 Undefined PRT Offline TA/TO
## 2003 2 none 0 Undefined ESP Offline TA/TO
## 2004 1 none 0 Undefined ESP Offline TA/TO
## 2005 2 none 0 Undefined ESP Offline TA/TO
## 2006 2 none 0 Undefined ESP Offline TA/TO
## 2007 2 none 0 Undefined ESP Offline TA/TO
## 2008 2 none 0 Undefined ESP Offline TA/TO
## 2009 2 none 0 Undefined ESP Offline TA/TO
## 2010 2 none 0 Undefined ESP Offline TA/TO
## 2011 2 none 0 Undefined ESP Offline TA/TO
## 2012 2 none 0 Undefined ESP Offline TA/TO
## 2013 2 none 0 Undefined ESP Offline TA/TO
## 2014 2 none 0 Undefined ESP Offline TA/TO
## 2015 2 none 0 Undefined ESP Offline TA/TO
## 2016 2 none 0 BB GBR Online TA
## 2017 2 children 0 BB POL Online TA
## 2018 2 none 0 BB DEU Offline TA/TO
## 2019 2 none 0 HB FRA Online TA
## 2020 2 children 0 BB FRA Online TA
## 2021 2 none 0 HB NLD Direct
## 2022 2 none 0 HB NLD Direct
## 2023 2 none 0 FB ESP Groups
## 2024 2 none 0 FB ESP Groups
## 2025 2 none 0 FB ESP Groups
## 2026 2 none 0 FB ESP Groups
## 2027 2 none 0 FB ESP Groups
## 2028 1 none 0 FB ESP Groups
## 2029 2 none 0 BB BRA Online TA
## 2030 2 none 0 FB ESP Groups
## 2031 2 none 0 FB ESP Groups
## 2032 2 none 0 FB ESP Groups
## 2033 2 none 0 BB PRT Direct
## 2034 2 none 0 FB ESP Groups
## 2035 2 none 0 FB ESP Groups
## 2036 2 none 0 FB ESP Groups
## 2037 2 none 0 FB ESP Groups
## 2038 1 none 0 FB ESP Groups
## 2039 2 none 0 BB PRT Corporate
## 2040 2 none 0 BB PRT Corporate
## 2041 2 children 1 BB PRT Corporate
## 2042 2 none 0 BB PRT Corporate
## 2043 2 none 0 BB PRT Corporate
## 2044 2 none 0 BB PRT Corporate
## 2045 2 none 0 BB PRT Corporate
## 2046 2 none 0 BB PRT Corporate
## 2047 2 none 0 BB PRT Corporate
## 2048 2 none 0 BB PRT Corporate
## 2049 2 children 0 BB PRT Corporate
## 2050 2 none 0 BB PRT Corporate
## 2051 2 none 0 BB PRT Corporate
## 2052 2 children 0 BB PRT Corporate
## 2053 2 none 0 BB PRT Corporate
## 2054 2 none 0 BB PRT Corporate
## 2055 2 none 0 BB PRT Corporate
## 2056 2 none 0 BB PRT Corporate
## 2057 2 none 0 BB PRT Corporate
## 2058 2 none 0 BB PRT Corporate
## 2059 2 none 0 BB PRT Corporate
## 2060 2 none 0 BB PRT Corporate
## 2061 2 children 1 HB PRT Online TA
## 2062 2 children 0 BB PRT Corporate
## 2063 2 none 0 HB GBR Online TA
## 2064 2 none 0 HB PRT Online TA
## 2065 2 none 0 BB PRT Corporate
## 2066 2 none 0 BB PRT Corporate
## 2067 2 none 0 BB PRT Corporate
## 2068 2 none 0 BB PRT Corporate
## 2069 2 children 1 BB PRT Corporate
## 2070 2 none 0 BB PRT Corporate
## 2071 2 none 0 BB PRT Corporate
## 2072 2 none 0 BB PRT Corporate
## 2073 2 children 0 BB PRT Corporate
## 2074 2 none 0 BB PRT Corporate
## 2075 2 none 0 BB PRT Corporate
## 2076 2 none 0 BB PRT Corporate
## 2077 2 none 0 HB PRT Online TA
## 2078 2 children 0 BB PRT Corporate
## 2079 2 children 1 BB PRT Corporate
## 2080 2 none 0 BB PRT Corporate
## 2081 3 none 0 HB PRT Direct
## 2082 2 none 0 BB PRT Corporate
## 2083 2 none 0 BB PRT Corporate
## 2084 2 none 0 BB PRT Corporate
## 2085 2 children 0 BB PRT Corporate
## 2086 2 none 0 BB FRA Offline TA/TO
## 2087 2 none 0 BB PRT Online TA
## 2088 2 none 0 BB PRT Online TA
## 2089 2 none 0 BB FRA Online TA
## 2090 2 none 0 BB ESP Offline TA/TO
## 2091 2 none 0 BB ESP Online TA
## 2092 2 none 0 HB ESP Offline TA/TO
## 2093 2 none 0 BB ESP Online TA
## 2094 2 none 0 BB POL Online TA
## 2095 1 none 0 BB NLD Online TA
## 2096 1 none 0 BB NLD Online TA
## 2097 1 none 0 BB NLD Online TA
## 2098 1 none 0 BB NLD Online TA
## 2099 1 none 0 BB NLD Online TA
## 2100 1 none 0 BB NLD Online TA
## 2101 2 none 0 BB GBR Offline TA/TO
## 2102 2 none 0 BB IRL Online TA
## 2103 1 none 0 BB NLD Online TA
## 2104 2 none 0 BB GBR Offline TA/TO
## 2105 2 none 0 BB PRT Offline TA/TO
## 2106 2 none 0 BB PRT Complementary
## 2107 2 none 0 BB PRT Online TA
## 2108 3 none 0 FB ESP Offline TA/TO
## 2109 2 none 0 BB PRT Offline TA/TO
## 2110 2 none 0 HB BEL Direct
## 2111 1 none 0 BB PRT Online TA
## 2112 2 none 0 BB PRT Online TA
## 2113 2 none 0 BB BEL Online TA
## 2114 2 none 0 BB PRT Online TA
## 2115 2 none 0 BB GBR Online TA
## 2116 2 none 0 BB GBR Online TA
## 2117 2 none 0 BB GBR Online TA
## 2118 2 none 0 BB PRT Online TA
## 2119 2 none 0 HB GBR Offline TA/TO
## 2120 2 none 0 BB CZE Online TA
## 2121 2 none 0 BB GBR Online TA
## 2122 2 none 0 BB GBR Offline TA/TO
## 2123 2 none 0 HB GBR Direct
## 2124 2 none 0 HB GBR Offline TA/TO
## 2125 2 none 0 BB NLD Offline TA/TO
## 2126 2 none 0 BB PRT Direct
## 2127 2 none 0 BB ESP Online TA
## 2128 2 none 0 HB ESP Offline TA/TO
## 2129 2 none 0 BB ESP Online TA
## 2130 2 none 0 BB GBR Offline TA/TO
## 2131 2 none 0 BB PRT Groups
## 2132 2 none 0 FB ESP Groups
## 2133 2 none 0 FB ESP Groups
## 2134 2 none 0 FB ESP Groups
## 2135 2 none 0 FB ESP Groups
## 2136 2 none 0 FB ESP Groups
## 2137 2 none 0 BB ESP Offline TA/TO
## 2138 2 none 0 FB ESP Groups
## 2139 2 none 0 FB ESP Groups
## 2140 3 none 0 FB ESP Groups
## 2141 1 none 0 FB PRT Groups
## 2142 2 none 0 FB ESP Groups
## 2143 2 none 0 FB ESP Groups
## 2144 1 none 0 FB ESP Groups
## 2145 2 none 0 FB ESP Groups
## 2146 2 none 0 FB ESP Groups
## 2147 2 none 0 FB ESP Groups
## 2148 1 none 0 FB PRT Groups
## 2149 1 none 0 HB ESP Online TA
## 2150 2 none 0 HB ESP Online TA
## 2151 2 none 0 HB ESP Online TA
## 2152 2 none 0 Undefined ESP Groups
## 2153 2 none 0 Undefined ESP Groups
## 2154 2 none 0 Undefined ESP Groups
## 2155 2 none 0 Undefined ESP Groups
## 2156 2 none 0 HB ESP Online TA
## 2157 2 none 0 Undefined ESP Groups
## 2158 2 none 0 Undefined ESP Groups
## 2159 3 none 0 BB ESP Offline TA/TO
## 2160 2 none 0 HB ESP Online TA
## 2161 2 none 0 HB ESP Online TA
## 2162 2 none 0 Undefined PRT Groups
## 2163 2 none 0 Undefined ESP Groups
## 2164 2 none 0 HB ESP Direct
## 2165 2 none 0 Undefined ESP Groups
## 2166 2 none 0 Undefined ESP Groups
## 2167 2 none 0 Undefined ESP Groups
## 2168 3 none 0 Undefined ESP Groups
## 2169 2 none 0 HB ESP Offline TA/TO
## 2170 2 none 0 BB GBR Online TA
## 2171 2 none 0 BB POL Offline TA/TO
## 2172 2 none 0 BB GBR Online TA
## 2173 2 none 0 HB PRT Online TA
## 2174 2 none 0 BB ZAF Online TA
## 2175 2 none 0 BB GBR Offline TA/TO
## 2176 2 none 0 BB GBR Offline TA/TO
## 2177 2 none 0 BB GBR Offline TA/TO
## 2178 2 none 0 HB GBR Offline TA/TO
## 2179 2 none 0 BB GBR Offline TA/TO
## 2180 2 none 0 BB GBR Offline TA/TO
## 2181 2 none 0 BB GBR Online TA
## 2182 2 none 0 BB IRL Online TA
## 2183 2 none 0 HB GBR Offline TA/TO
## 2184 2 none 0 BB GBR Online TA
## 2185 1 none 0 BB GBR Offline TA/TO
## 2186 2 none 0 BB GBR Online TA
## 2187 2 none 0 BB PRT Online TA
## 2188 1 none 0 BB ESP Offline TA/TO
## 2189 2 none 0 BB PRT Offline TA/TO
## 2190 2 none 0 BB GBR Online TA
## 2191 2 none 0 BB GBR Offline TA/TO
## 2192 2 none 0 BB DEU Offline TA/TO
## 2193 2 none 0 BB PRT Online TA
## 2194 2 none 0 BB PRT Online TA
## 2195 2 none 0 BB ITA Offline TA/TO
## 2196 2 none 0 BB FIN Online TA
## 2197 2 none 0 BB KOR Online TA
## 2198 2 none 0 BB GBR Offline TA/TO
## 2199 2 none 0 BB PRT Online TA
## 2200 2 none 0 HB GBR Online TA
## 2201 2 none 0 HB GBR Offline TA/TO
## 2202 1 none 0 BB CN Online TA
## 2203 2 none 0 BB PRT Online TA
## 2204 3 none 0 BB ESP Offline TA/TO
## 2205 2 none 0 BB GBR Direct
## 2206 2 none 0 HB IRL Offline TA/TO
## 2207 1 none 0 BB FRA Online TA
## 2208 2 none 0 HB IRL Offline TA/TO
## 2209 2 none 0 HB IRL Offline TA/TO
## 2210 2 none 0 HB PRT Offline TA/TO
## 2211 2 none 0 BB IRL Offline TA/TO
## 2212 2 none 0 HB IRL Offline TA/TO
## 2213 2 none 0 HB IRL Offline TA/TO
## 2214 2 none 0 HB IRL Groups
## 2215 2 none 0 HB IRL Offline TA/TO
## 2216 1 none 0 HB IRL Offline TA/TO
## 2217 2 none 0 HB AUS Offline TA/TO
## 2218 2 none 0 HB IRL Offline TA/TO
## 2219 1 none 0 BB PRT Online TA
## 2220 2 none 0 HB ITA Online TA
## 2221 2 none 0 HB PRT Online TA
## 2222 2 children 0 HB PRT Online TA
## 2223 2 none 0 BB BRA Online TA
## 2224 1 none 0 BB GBR Online TA
## 2225 2 none 0 BB IRL Offline TA/TO
## 2226 2 none 0 HB FRA Offline TA/TO
## 2227 1 none 0 BB IRL Online TA
## 2228 2 none 0 BB FRA Offline TA/TO
## 2229 2 none 0 BB IRL Online TA
## 2230 2 none 0 BB PRT Direct
## 2231 1 none 0 BB PRT Direct
## 2232 3 none 0 BB PRT Direct
## 2233 2 none 0 BB PRT Online TA
## 2234 2 none 0 BB PRT Direct
## 2235 2 children 1 BB GBR Direct
## 2236 2 none 0 BB PRT Direct
## 2237 3 none 0 BB GBR Direct
## 2238 2 none 0 HB ESP Offline TA/TO
## 2239 2 none 0 BB GBR Offline TA/TO
## 2240 2 children 0 BB POL Online TA
## 2241 2 children 0 BB POL Online TA
## 2242 2 none 0 BB GBR Offline TA/TO
## 2243 2 none 0 BB POL Online TA
## 2244 2 none 0 HB GBR Offline TA/TO
## 2245 2 none 0 BB CHE Online TA
## 2246 2 none 0 BB PRT Online TA
## 2247 2 none 0 BB LTU Online TA
## 2248 2 none 0 BB ESP Offline TA/TO
## 2249 1 none 0 BB ESP Offline TA/TO
## 2250 2 none 0 BB ESP Offline TA/TO
## 2251 2 none 0 BB ESP Offline TA/TO
## 2252 2 children 1 BB PRT Online TA
## 2253 2 children 0 BB ESP Online TA
## 2254 2 none 0 BB GBR Offline TA/TO
## 2255 2 none 0 BB ESP Online TA
## 2256 2 none 0 BB CRI Online TA
## 2257 2 none 0 BB CRI Online TA
## 2258 2 none 0 BB FRA Direct
## 2259 2 none 0 BB GBR Offline TA/TO
## 2260 2 none 0 BB GBR Offline TA/TO
## 2261 1 children 0 BB GBR Online TA
## 2262 2 none 0 HB GBR Offline TA/TO
## 2263 2 none 0 BB PRT Online TA
## 2264 2 none 0 BB FRA Online TA
## 2265 2 none 0 BB PRT Online TA
## 2266 2 children 0 HB PRT Direct
## 2267 2 none 0 Undefined PRT Groups
## 2268 2 none 0 Undefined PRT Groups
## 2269 2 none 0 Undefined PRT Groups
## 2270 2 none 0 Undefined PRT Groups
## 2271 2 none 0 BB PRT Online TA
## 2272 2 none 0 Undefined PRT Groups
## 2273 2 none 0 Undefined PRT Groups
## 2274 1 none 0 HB PRT Groups
## 2275 1 none 0 HB PRT Groups
## 2276 2 none 0 Undefined PRT Groups
## 2277 2 none 0 Undefined PRT Groups
## 2278 2 none 0 Undefined PRT Groups
## 2279 2 none 0 Undefined PRT Groups
## 2280 2 none 0 Undefined PRT Groups
## 2281 2 none 0 Undefined PRT Groups
## 2282 1 none 0 Undefined PRT Groups
## 2283 2 none 0 Undefined PRT Groups
## 2284 2 none 0 Undefined PRT Groups
## 2285 2 none 0 Undefined PRT Groups
## 2286 2 none 0 Undefined PRT Groups
## 2287 2 none 0 Undefined PRT Groups
## 2288 1 none 0 Undefined PRT Groups
## 2289 2 none 0 Undefined PRT Groups
## 2290 1 none 0 HB PRT Groups
## 2291 1 none 0 HB PRT Groups
## 2292 1 none 0 HB PRT Groups
## 2293 2 none 0 Undefined PRT Groups
## 2294 2 none 0 Undefined PRT Groups
## 2295 2 none 0 Undefined PRT Groups
## 2296 2 none 0 Undefined PRT Groups
## 2297 3 none 0 Undefined PRT Groups
## 2298 2 none 0 Undefined PRT Groups
## 2299 2 none 0 BB PRT Online TA
## 2300 2 children 0 BB PRT Offline TA/TO
## 2301 2 none 0 Undefined PRT Groups
## 2302 2 none 0 Undefined PRT Groups
## 2303 2 none 0 Undefined PRT Groups
## 2304 2 none 0 Undefined PRT Groups
## 2305 2 none 0 Undefined PRT Groups
## 2306 2 none 0 Undefined PRT Groups
## 2307 2 none 0 Undefined PRT Groups
## 2308 1 none 0 Undefined PRT Groups
## 2309 2 none 0 Undefined PRT Groups
## 2310 2 none 0 Undefined PRT Groups
## 2311 2 none 0 Undefined PRT Groups
## 2312 2 none 0 Undefined PRT Groups
## 2313 2 none 0 Undefined PRT Groups
## 2314 1 none 0 Undefined PRT Groups
## 2315 1 none 0 Undefined PRT Groups
## 2316 2 none 0 Undefined PRT Groups
## 2317 2 none 0 Undefined PRT Groups
## 2318 1 none 0 HB PRT Groups
## 2319 1 none 0 HB PRT Groups
## 2320 2 none 0 HB PRT Direct
## 2321 2 none 0 BB PRT Direct
## 2322 2 none 0 BB PRT Online TA
## 2323 2 children 0 HB PRT Online TA
## 2324 3 none 0 BB PRT Direct
## 2325 2 none 0 BB PRT Direct
## 2326 2 children 0 BB PRT Offline TA/TO
## 2327 2 children 0 BB PRT Online TA
## 2328 2 none 0 BB PRT Direct
## 2329 2 none 0 BB PRT Direct
## 2330 2 children 0 BB PRT Direct
## 2331 2 none 0 BB PRT Direct
## 2332 2 none 0 HB GBR Offline TA/TO
## 2333 2 none 0 HB GBR Offline TA/TO
## 2334 2 none 0 HB GBR Offline TA/TO
## 2335 2 none 0 BB RUS Online TA
## 2336 2 children 0 BB RUS Online TA
## 2337 2 none 0 HB ARG Online TA
## 2338 2 children 0 HB ARG Online TA
## 2339 2 none 0 BB FRA Online TA
## 2340 2 none 0 HB GBR Online TA
## 2341 2 none 0 BB DEU Online TA
## 2342 2 none 0 BB BRA Online TA
## 2343 2 none 0 BB PRT Offline TA/TO
## 2344 2 none 0 BB IRL Offline TA/TO
## 2345 2 none 0 BB GBR Online TA
## 2346 2 none 0 BB DEU Offline TA/TO
## 2347 2 none 0 BB GBR Offline TA/TO
## 2348 2 none 0 BB PRT Online TA
## 2349 2 none 0 BB USA Direct
## 2350 2 none 0 HB GBR Online TA
## 2351 2 none 0 BB GBR Online TA
## 2352 2 none 0 BB KOR Online TA
## 2353 2 none 0 BB GBR Offline TA/TO
## 2354 2 none 0 BB KOR Online TA
## 2355 2 none 0 BB PRT Direct
## 2356 1 none 0 BB PRT Corporate
## 2357 2 none 0 BB GBR Online TA
## 2358 2 none 0 BB GBR Direct
## 2359 2 none 0 BB GBR Offline TA/TO
## 2360 2 none 0 BB ESP Offline TA/TO
## 2361 2 none 0 BB ESP Offline TA/TO
## 2362 2 none 0 HB GBR Offline TA/TO
## 2363 2 none 0 BB IRL Offline TA/TO
## 2364 2 none 0 BB IRL Offline TA/TO
## 2365 2 none 0 BB DNK Online TA
## 2366 2 none 0 BB CHN Online TA
## 2367 2 none 0 BB CHN Online TA
## 2368 2 none 0 BB IRL Online TA
## 2369 2 none 0 BB IRL Online TA
## 2370 2 none 0 HB FRA Groups
## 2371 2 none 0 HB FRA Groups
## 2372 2 none 0 HB FRA Groups
## 2373 2 none 0 HB FRA Groups
## 2374 2 none 0 HB FRA Groups
## 2375 2 none 0 HB PRT Groups
## 2376 2 none 0 HB FRA Groups
## 2377 2 none 0 HB FRA Groups
## 2378 2 none 0 HB FRA Groups
## 2379 2 none 0 HB FRA Groups
## 2380 1 none 0 BB PRT Offline TA/TO
## 2381 2 none 0 BB NLD Online TA
## 2382 2 none 0 BB NLD Online TA
## 2383 1 none 0 BB NLD Online TA
## 2384 2 none 0 BB GBR Offline TA/TO
## 2385 2 none 0 Undefined ESP Groups
## 2386 3 none 0 Undefined ESP Groups
## 2387 1 none 0 Undefined ESP Groups
## 2388 2 none 0 Undefined ESP Groups
## 2389 2 none 0 Undefined ESP Groups
## 2390 2 none 0 Undefined ESP Groups
## 2391 2 none 0 Undefined ESP Groups
## 2392 2 none 0 Undefined ESP Groups
## 2393 2 none 0 Undefined ESP Groups
## 2394 2 none 0 Undefined ESP Groups
## 2395 2 none 0 Undefined ESP Groups
## 2396 1 none 0 Undefined ESP Groups
## 2397 2 none 0 Undefined ESP Groups
## 2398 2 none 0 Undefined ESP Groups
## 2399 2 none 0 Undefined ESP Groups
## 2400 3 none 0 Undefined ESP Groups
## 2401 1 none 0 Undefined ESP Groups
## 2402 2 none 0 Undefined ESP Groups
## 2403 2 none 0 Undefined ESP Groups
## 2404 2 none 0 Undefined ESP Groups
## 2405 2 none 0 Undefined ESP Groups
## 2406 3 none 0 Undefined ESP Groups
## 2407 1 none 0 Undefined ESP Groups
## 2408 2 none 0 BB GBR Online TA
## 2409 2 none 0 BB GBR Online TA
## 2410 2 none 0 BB FRA Offline TA/TO
## 2411 1 none 0 BB BRA Online TA
## 2412 2 children 0 BB HUN Online TA
## 2413 2 none 0 BB POL Offline TA/TO
## 2414 2 none 0 BB POL Offline TA/TO
## 2415 2 none 0 BB GBR Online TA
## 2416 2 children 0 BB GBR Online TA
## 2417 2 none 0 BB GBR Online TA
## 2418 2 none 0 BB IRL Offline TA/TO
## 2419 2 none 0 BB PRT Online TA
## 2420 1 none 0 BB NLD Offline TA/TO
## 2421 2 none 0 FB IRL Offline TA/TO
## 2422 1 none 0 BB NLD Offline TA/TO
## 2423 2 none 0 BB IRL Direct
## 2424 2 none 0 HB GBR Offline TA/TO
## 2425 2 none 0 HB GBR Offline TA/TO
## 2426 2 none 0 HB GBR Offline TA/TO
## 2427 1 none 0 BB GBR Offline TA/TO
## 2428 1 none 0 BB GBR Offline TA/TO
## 2429 2 none 0 HB GBR Direct
## 2430 1 none 0 BB PRT Online TA
## 2431 2 none 0 FB ESP Groups
## 2432 2 none 0 FB ESP Groups
## 2433 2 none 0 FB ESP Groups
## 2434 2 none 0 FB ESP Groups
## 2435 2 none 0 FB ESP Groups
## 2436 2 none 0 FB ESP Groups
## 2437 2 none 0 FB ESP Groups
## 2438 2 none 0 FB ESP Groups
## 2439 2 none 0 FB ESP Groups
## 2440 2 none 0 FB ESP Groups
## 2441 2 none 0 FB ESP Groups
## 2442 2 none 0 FB ESP Groups
## 2443 2 none 0 FB ESP Groups
## 2444 2 none 0 FB ESP Groups
## 2445 2 none 0 FB ESP Groups
## 2446 2 none 0 FB ESP Groups
## 2447 2 none 0 FB ESP Groups
## 2448 2 none 0 FB ESP Groups
## 2449 2 none 0 FB ESP Groups
## 2450 2 none 0 FB ESP Groups
## 2451 2 none 0 FB ESP Groups
## 2452 2 none 0 FB ESP Groups
## 2453 2 none 0 BB CN Offline TA/TO
## 2454 1 none 0 BB PRT Corporate
## 2455 2 none 0 BB FRA Offline TA/TO
## 2456 2 none 0 HB GBR Offline TA/TO
## 2457 1 none 0 BB PRT Direct
## 2458 2 none 0 BB DEU Online TA
## 2459 2 none 0 BB DEU Online TA
## 2460 2 none 0 BB GBR Offline TA/TO
## 2461 2 none 0 BB GBR Offline TA/TO
## 2462 1 none 0 BB PRT Corporate
## 2463 2 none 0 HB PRT Offline TA/TO
## 2464 2 none 0 BB GBR Online TA
## 2465 2 none 0 BB CN Direct
## 2466 1 none 0 BB PRT Direct
## 2467 2 none 0 BB BEL Online TA
## 2468 2 none 0 BB ESP Offline TA/TO
## 2469 2 none 0 BB GBR Online TA
## 2470 2 none 0 BB CN Offline TA/TO
## 2471 2 none 0 BB PRT Online TA
## 2472 2 none 0 BB GBR Offline TA/TO
## 2473 2 none 0 BB GBR Offline TA/TO
## 2474 2 none 0 FB ESP Offline TA/TO
## 2475 2 none 0 FB ESP Offline TA/TO
## 2476 2 none 0 BB GBR Online TA
## 2477 2 none 0 HB ESP Online TA
## 2478 2 none 0 BB IRL Online TA
## 2479 2 none 0 BB PRT Direct
## 2480 2 none 0 BB ESP Online TA
## 2481 3 none 0 BB GBR Offline TA/TO
## 2482 2 none 0 BB IRL Offline TA/TO
## 2483 2 none 0 BB PRT Online TA
## 2484 2 none 0 Undefined ESP Groups
## 2485 2 none 0 Undefined ESP Groups
## 2486 2 none 0 Undefined PRT Groups
## 2487 3 none 0 Undefined ESP Groups
## 2488 2 none 0 BB ESP Online TA
## 2489 2 none 0 Undefined PRT Offline TA/TO
## 2490 2 none 0 Undefined ESP Offline TA/TO
## 2491 2 none 0 Undefined PRT Offline TA/TO
## 2492 1 none 0 Undefined ESP Offline TA/TO
## 2493 1 none 0 Undefined ESP Offline TA/TO
## 2494 2 none 0 Undefined PRT Groups
## 2495 2 none 0 Undefined ESP Groups
## 2496 2 none 0 Undefined ESP Groups
## 2497 2 none 0 Undefined ESP Groups
## 2498 2 none 0 Undefined ESP Offline TA/TO
## 2499 2 none 0 Undefined ESP Offline TA/TO
## 2500 3 none 0 Undefined ESP Offline TA/TO
## 2501 2 none 0 Undefined PRT Offline TA/TO
## 2502 2 none 0 Undefined ESP Offline TA/TO
## 2503 2 none 0 Undefined PRT Offline TA/TO
## 2504 2 none 0 Undefined ESP Groups
## 2505 2 none 0 Undefined ESP Groups
## 2506 2 none 0 Undefined ESP Offline TA/TO
## 2507 2 none 0 Undefined ESP Offline TA/TO
## 2508 2 none 0 Undefined ESP Groups
## 2509 2 none 0 Undefined ESP Groups
## 2510 2 none 0 Undefined ESP Groups
## 2511 2 none 0 Undefined ESP Offline TA/TO
## 2512 2 none 0 Undefined ESP Offline TA/TO
## 2513 2 none 0 Undefined ESP Groups
## 2514 2 none 0 Undefined ESP Groups
## 2515 2 none 0 Undefined ESP Groups
## 2516 2 none 0 Undefined PRT Groups
## 2517 2 none 0 Undefined ESP Offline TA/TO
## 2518 2 none 0 Undefined PRT Offline TA/TO
## 2519 2 none 0 Undefined ESP Offline TA/TO
## 2520 3 none 0 Undefined ESP Offline TA/TO
## 2521 1 none 0 Undefined ESP Offline TA/TO
## 2522 2 none 0 Undefined ESP Groups
## 2523 2 none 0 Undefined ESP Groups
## 2524 2 none 0 Undefined ESP Groups
## 2525 2 none 0 Undefined ESP Offline TA/TO
## 2526 2 none 0 Undefined ESP Offline TA/TO
## 2527 2 none 0 BB ESP Online TA
## 2528 2 none 0 Undefined ESP Groups
## 2529 2 none 0 Undefined ESP Groups
## 2530 2 none 0 Undefined ESP Groups
## 2531 2 none 0 Undefined ESP Groups
## 2532 3 none 0 Undefined PRT Groups
## 2533 2 none 0 Undefined ESP Offline TA/TO
## 2534 2 none 0 Undefined ESP Offline TA/TO
## 2535 2 none 0 Undefined ESP Offline TA/TO
## 2536 3 none 0 Undefined ESP Offline TA/TO
## 2537 2 none 0 Undefined ESP Groups
## 2538 2 none 0 Undefined PRT Groups
## 2539 2 none 0 SC ESP Offline TA/TO
## 2540 2 none 0 SC ESP Offline TA/TO
## 2541 2 none 0 BB PRT Online TA
## 2542 2 none 0 BB PRT Online TA
## 2543 2 none 0 BB GBR Offline TA/TO
## 2544 2 none 0 BB GBR Offline TA/TO
## 2545 2 none 0 HB LUX Offline TA/TO
## 2546 2 none 0 HB LUX Offline TA/TO
## 2547 2 none 0 HB LUX Offline TA/TO
## 2548 2 none 0 HB LUX Offline TA/TO
## 2549 2 none 0 HB LUX Offline TA/TO
## 2550 2 none 0 HB LUX Offline TA/TO
## 2551 2 none 0 HB PRT Offline TA/TO
## 2552 1 none 0 BB GBR Groups
## 2553 2 none 0 BB GBR Groups
## 2554 2 none 0 BB GBR Groups
## 2555 1 none 0 BB DEU Groups
## 2556 3 none 0 BB GBR Groups
## 2557 1 none 0 BB GBR Groups
## 2558 2 children 0 BB GBR Groups
## 2559 2 none 0 BB GBR Groups
## 2560 2 none 0 BB GBR Groups
## 2561 2 none 0 BB PRT Groups
## 2562 2 none 0 BB GBR Direct
## 2563 2 none 0 BB GBR Groups
## 2564 2 none 0 BB GBR Groups
## 2565 2 children 1 BB GBR Groups
## 2566 2 none 0 BB GBR Groups
## 2567 2 none 0 BB PRT Groups
## 2568 2 none 0 BB GBR Groups
## 2569 2 none 0 BB PRT Groups
## 2570 2 none 0 BB PRT Groups
## 2571 2 none 0 BB GBR Groups
## 2572 2 none 0 BB GBR Groups
## 2573 2 none 0 BB PRT Groups
## 2574 2 none 0 BB GBR Groups
## 2575 2 none 0 BB PRT Groups
## 2576 2 none 0 BB PRT Groups
## 2577 2 none 0 BB GBR Groups
## 2578 2 none 0 BB PRT Groups
## 2579 2 none 0 BB PRT Groups
## 2580 2 none 0 BB PRT Groups
## 2581 2 none 0 BB GBR Groups
## 2582 1 none 0 BB AUT Groups
## 2583 2 none 0 BB GBR Groups
## 2584 2 none 0 BB GBR Groups
## 2585 2 none 0 BB POL Groups
## 2586 2 none 0 BB BEL Groups
## 2587 2 none 0 BB GBR Groups
## 2588 2 none 0 BB PRT Groups
## 2589 2 none 0 BB GBR Groups
## 2590 1 none 0 BB NLD Groups
## 2591 2 none 0 BB GBR Groups
## 2592 3 none 0 BB PRT Groups
## 2593 1 none 0 BB BEL Groups
## 2594 2 children 0 BB PRT Groups
## 2595 2 none 0 BB PRT Groups
## 2596 2 none 0 BB GBR Groups
## 2597 1 none 0 BB PRT Groups
## 2598 1 none 0 BB GBR Groups
## 2599 2 none 0 BB PRT Groups
## 2600 2 none 0 BB PRT Groups
## 2601 3 none 0 BB GBR Groups
## 2602 2 none 0 BB PRT Groups
## 2603 2 none 0 BB GBR Groups
## 2604 2 none 0 BB GBR Groups
## 2605 2 none 0 BB PRT Groups
## 2606 2 none 0 BB GBR Groups
## 2607 1 none 0 BB PRT Groups
## 2608 1 none 0 BB PRT Groups
## 2609 2 none 0 BB NLD Groups
## 2610 2 none 0 BB PRT Groups
## 2611 2 none 0 BB GBR Groups
## 2612 2 none 0 BB PRT Groups
## 2613 1 none 0 BB GBR Groups
## 2614 1 none 0 BB BEL Groups
## 2615 2 none 0 BB GBR Groups
## 2616 2 none 0 BB GBR Groups
## 2617 2 none 0 BB NLD Groups
## 2618 2 none 0 BB NLD Groups
## 2619 2 none 0 BB GBR Groups
## 2620 2 none 0 BB BEL Groups
## 2621 1 none 0 BB GBR Groups
## 2622 2 none 0 BB GBR Offline TA/TO
## 2623 2 none 0 BB IRL Offline TA/TO
## 2624 2 none 0 BB GBR Offline TA/TO
## 2625 2 none 0 BB CHE Online TA
## 2626 2 none 0 BB FRA Online TA
## 2627 2 none 0 BB GBR Online TA
## 2628 2 none 0 BB GBR Online TA
## 2629 2 none 0 BB GBR Groups
## 2630 2 none 0 BB PRT Groups
## 2631 2 children 0 BB PRT Offline TA/TO
## 2632 2 none 0 HB DEU Groups
## 2633 1 none 0 HB DEU Groups
## 2634 2 none 0 HB DEU Groups
## 2635 2 none 0 HB PRT Groups
## 2636 2 none 0 HB DEU Groups
## 2637 1 none 0 HB DEU Groups
## 2638 1 none 0 HB DEU Groups
## 2639 1 none 0 HB DEU Groups
## 2640 2 none 0 HB DEU Groups
## 2641 2 none 0 HB DEU Groups
## 2642 2 none 0 HB DEU Groups
## 2643 2 none 0 HB DEU Groups
## 2644 2 none 0 HB DEU Groups
## 2645 1 none 0 HB DEU Groups
## 2646 1 none 0 HB PRT Groups
## 2647 1 none 0 HB DEU Groups
## 2648 2 none 0 HB DEU Groups
## 2649 2 none 0 HB DEU Groups
## 2650 1 none 0 HB DEU Groups
## 2651 2 none 0 HB PRT Groups
## 2652 2 none 0 HB DEU Groups
## 2653 1 none 0 HB DEU Groups
## 2654 1 none 0 HB DEU Groups
## 2655 2 none 0 HB DEU Groups
## 2656 2 none 0 HB DEU Groups
## 2657 2 none 0 HB DEU Groups
## 2658 1 none 0 HB DEU Groups
## 2659 2 none 0 HB PRT Groups
## 2660 1 none 0 HB DEU Groups
## 2661 2 none 0 HB DEU Groups
## 2662 2 none 0 HB DEU Groups
## 2663 2 none 0 HB DEU Groups
## 2664 1 none 0 HB DEU Groups
## 2665 1 none 0 HB DEU Groups
## 2666 1 none 0 HB DEU Groups
## 2667 1 none 0 HB DEU Groups
## 2668 2 none 0 HB DEU Groups
## 2669 2 none 0 HB DEU Groups
## 2670 2 none 0 HB DEU Groups
## 2671 2 none 0 SC DEU Groups
## 2672 2 none 0 BB DEU Online TA
## 2673 1 none 0 BB GBR Offline TA/TO
## 2674 1 none 0 BB GBR Offline TA/TO
## 2675 2 none 0 BB PRT Online TA
## 2676 2 children 0 HB FRA Direct
## 2677 1 none 0 HB GBR Groups
## 2678 1 none 0 HB GBR Groups
## 2679 2 none 0 HB GBR Groups
## 2680 1 none 0 HB GBR Groups
## 2681 2 none 0 HB GBR Groups
## 2682 2 none 0 HB GBR Groups
## 2683 2 none 0 HB GBR Groups
## 2684 2 none 0 HB BRA Groups
## 2685 1 none 0 HB GBR Groups
## 2686 2 none 0 HB GBR Groups
## 2687 2 none 0 HB GBR Groups
## 2688 2 none 0 HB GBR Groups
## 2689 2 none 0 HB GBR Groups
## 2690 1 none 0 HB GBR Groups
## 2691 1 none 0 HB GBR Groups
## 2692 2 none 0 HB GBR Groups
## 2693 2 none 0 SC GBR Groups
## 2694 2 none 0 HB GBR Groups
## 2695 1 none 0 BB PRT Corporate
## 2696 2 none 0 BB PRT Online TA
## 2697 2 none 0 BB FRA Online TA
## 2698 2 none 0 HB GBR Online TA
## 2699 2 none 0 BB IRL Online TA
## 2700 2 none 0 BB IRL Direct
## 2701 2 none 0 HB DEU Groups
## 2702 2 none 0 HB DEU Groups
## 2703 1 none 0 HB PRT Groups
## 2704 2 none 0 HB DEU Groups
## 2705 2 none 0 HB DEU Groups
## 2706 1 none 0 HB DEU Groups
## 2707 1 none 0 HB DEU Groups
## 2708 2 none 0 HB DEU Groups
## 2709 1 none 0 HB DEU Groups
## 2710 2 none 0 HB DEU Groups
## 2711 2 none 0 HB ESP Groups
## 2712 2 none 0 HB DEU Groups
## 2713 2 none 0 HB DEU Groups
## 2714 2 none 0 HB DEU Groups
## 2715 2 none 0 HB DEU Groups
## 2716 2 none 0 HB DEU Groups
## 2717 1 none 0 HB DEU Groups
## 2718 1 none 0 HB DEU Groups
## 2719 2 none 0 HB DEU Groups
## 2720 1 none 0 HB DEU Groups
## 2721 1 none 0 HB DEU Groups
## 2722 2 none 0 HB DEU Groups
## 2723 1 none 0 HB DEU Groups
## 2724 2 none 0 HB DEU Groups
## 2725 2 none 0 HB DEU Groups
## 2726 1 none 0 HB DEU Groups
## 2727 1 none 0 HB DEU Groups
## 2728 1 none 0 HB DEU Groups
## 2729 2 none 0 SC DEU Groups
## 2730 2 none 0 HB FRA Offline TA/TO
## 2731 3 none 0 HB FRA Offline TA/TO
## 2732 2 none 0 BB GBR Offline TA/TO
## 2733 2 none 0 BB PRT Online TA
## 2734 2 none 0 BB GBR Direct
## 2735 2 none 0 HB DEU Groups
## 2736 1 none 0 HB DEU Groups
## 2737 1 none 0 HB DEU Groups
## 2738 2 none 0 HB DEU Groups
## 2739 1 none 0 HB DEU Groups
## 2740 2 none 0 HB DEU Groups
## 2741 2 none 0 HB DEU Groups
## 2742 2 none 0 HB DEU Groups
## 2743 2 none 0 HB DEU Groups
## 2744 2 none 0 HB DEU Groups
## 2745 2 none 0 HB DEU Groups
## 2746 2 none 0 HB DEU Groups
## 2747 2 none 0 SC DEU Groups
## 2748 2 none 0 HB DEU Groups
## 2749 2 none 0 HB DEU Groups
## 2750 2 none 0 HB DEU Groups
## 2751 1 none 0 HB DEU Groups
## 2752 1 none 0 HB DEU Groups
## 2753 1 none 0 HB DEU Groups
## 2754 2 none 0 HB DEU Groups
## 2755 1 none 0 HB DEU Groups
## 2756 2 none 0 HB DEU Groups
## 2757 1 none 0 HB DEU Groups
## 2758 2 none 0 HB DEU Groups
## 2759 2 none 0 HB DEU Groups
## 2760 2 none 0 HB DEU Groups
## 2761 1 none 0 HB DEU Groups
## 2762 1 none 0 HB DEU Groups
## 2763 2 none 0 HB DEU Groups
## 2764 1 none 0 HB GBR Offline TA/TO
## 2765 1 none 0 HB GBR Offline TA/TO
## 2766 2 none 0 BB GBR Offline TA/TO
## 2767 1 none 0 HB GBR Offline TA/TO
## 2768 1 none 0 HB BRA Offline TA/TO
## 2769 2 none 0 HB GBR Offline TA/TO
## 2770 2 none 0 BB IRL Direct
## 2771 2 none 0 BB IRL Direct
## 2772 2 none 0 HB POL Offline TA/TO
## 2773 2 none 0 BB ITA Online TA
## 2774 2 none 0 BB ITA Online TA
## 2775 2 none 0 BB GBR Offline TA/TO
## 2776 2 none 0 Undefined PRT Offline TA/TO
## 2777 2 none 0 Undefined PRT Offline TA/TO
## 2778 2 none 0 Undefined PRT Offline TA/TO
## 2779 2 none 0 Undefined PRT Offline TA/TO
## 2780 2 none 0 Undefined PRT Offline TA/TO
## 2781 2 none 0 Undefined PRT Offline TA/TO
## 2782 2 none 0 Undefined PRT Offline TA/TO
## 2783 2 none 0 Undefined PRT Offline TA/TO
## 2784 2 none 0 Undefined PRT Offline TA/TO
## 2785 2 none 0 Undefined PRT Offline TA/TO
## 2786 2 none 0 Undefined PRT Offline TA/TO
## 2787 2 none 0 Undefined PRT Offline TA/TO
## 2788 2 none 0 Undefined PRT Offline TA/TO
## 2789 2 none 0 Undefined PRT Offline TA/TO
## 2790 2 none 0 Undefined PRT Offline TA/TO
## 2791 2 none 0 Undefined PRT Offline TA/TO
## 2792 2 none 0 Undefined PRT Offline TA/TO
## 2793 2 none 0 Undefined PRT Offline TA/TO
## 2794 3 none 0 Undefined PRT Offline TA/TO
## 2795 2 none 0 Undefined PRT Offline TA/TO
## 2796 2 none 0 Undefined PRT Offline TA/TO
## 2797 1 none 0 Undefined PRT Offline TA/TO
## 2798 2 none 0 Undefined PRT Offline TA/TO
## 2799 2 none 0 Undefined PRT Offline TA/TO
## 2800 2 none 0 Undefined PRT Offline TA/TO
## 2801 2 none 0 Undefined PRT Offline TA/TO
## 2802 2 none 0 Undefined PRT Offline TA/TO
## 2803 2 none 0 FB PRT Online TA
## 2804 2 none 0 BB PRT Direct
## 2805 2 none 0 BB IRL Offline TA/TO
## 2806 2 none 0 BB IRL Offline TA/TO
## 2807 2 none 0 BB GBR Offline TA/TO
## 2808 1 none 0 BB PRT Groups
## 2809 2 none 0 BB FRA Groups
## 2810 2 none 0 BB FRA Groups
## 2811 1 none 0 BB PRT Groups
## 2812 1 none 0 BB FRA Groups
## 2813 2 none 0 HB GBR Offline TA/TO
## 2814 2 none 0 BB GBR Online TA
## 2815 2 none 0 BB GBR Offline TA/TO
## 2816 2 none 0 HB DEU Groups
## 2817 2 none 0 HB DEU Groups
## 2818 2 none 0 HB PRT Groups
## 2819 2 none 0 HB DEU Groups
## 2820 2 none 0 HB DEU Groups
## 2821 2 none 0 HB DEU Groups
## 2822 2 none 0 HB DEU Groups
## 2823 2 none 0 HB GBR Offline TA/TO
## 2824 2 none 0 BB IRL Online TA
## 2825 2 none 0 BB NLD Offline TA/TO
## 2826 2 none 0 BB GBR Offline TA/TO
## 2827 2 children 1 HB GBR Offline TA/TO
## 2828 2 none 0 BB GBR Offline TA/TO
## 2829 2 none 0 HB DEU Groups
## 2830 2 none 0 HB DEU Groups
## 2831 1 none 0 HB DEU Groups
## 2832 2 none 0 HB DEU Groups
## 2833 2 none 0 HB DEU Groups
## 2834 2 none 0 HB DEU Groups
## 2835 2 none 0 HB DEU Groups
## 2836 2 none 0 HB DEU Groups
## 2837 2 none 0 HB DEU Groups
## 2838 2 none 0 HB DEU Groups
## 2839 2 none 0 HB DEU Groups
## 2840 2 none 0 HB DEU Groups
## 2841 2 none 0 HB DEU Groups
## 2842 1 none 0 HB DEU Groups
## 2843 1 none 0 HB DEU Groups
## 2844 2 none 0 HB DEU Groups
## 2845 2 none 0 HB DEU Groups
## 2846 2 none 0 HB DEU Groups
## 2847 2 none 0 HB DEU Groups
## 2848 2 none 0 HB DEU Groups
## 2849 1 none 0 HB DEU Groups
## 2850 2 none 0 HB DEU Groups
## 2851 2 none 0 HB DEU Groups
## 2852 1 none 0 HB DEU Groups
## 2853 2 none 0 HB DEU Groups
## 2854 1 none 0 HB DEU Groups
## 2855 2 none 0 BB IRL Online TA
## 2856 2 none 0 BB IRL Online TA
## 2857 2 none 0 HB GBR Offline TA/TO
## 2858 2 none 0 BB GBR Offline TA/TO
## 2859 2 none 0 HB GBR Offline TA/TO
## 2860 2 none 0 BB GBR Offline TA/TO
## 2861 2 none 0 HB GBR Offline TA/TO
## 2862 2 none 0 BB GBR Offline TA/TO
## 2863 2 none 0 BB PRT Online TA
## 2864 2 none 0 BB IRL Online TA
## 2865 2 none 0 BB ESP Online TA
## 2866 2 none 0 BB IRL Online TA
## 2867 2 none 0 BB IRL Online TA
## 2868 1 none 0 BB GBR Offline TA/TO
## 2869 2 none 0 BB GBR Offline TA/TO
## 2870 1 none 0 BB PRT Offline TA/TO
## 2871 2 none 0 BB GBR Offline TA/TO
## 2872 2 none 0 BB IRL Offline TA/TO
## 2873 2 none 0 BB BRA Online TA
## 2874 2 none 0 HB GBR Offline TA/TO
## 2875 2 none 0 BB GBR Online TA
## 2876 2 none 0 BB GBR Online TA
## 2877 2 none 0 BB GBR Offline TA/TO
## 2878 2 none 0 BB GBR Offline TA/TO
## 2879 2 none 0 HB GBR Offline TA/TO
## 2880 2 none 0 BB IRL Offline TA/TO
## 2881 2 none 0 BB IRL Online TA
## 2882 2 none 0 BB FRA Groups
## 2883 2 none 0 BB PRT Groups
## 2884 1 none 0 BB FRA Groups
## 2885 1 none 0 BB PRT Groups
## 2886 2 none 0 BB FRA Groups
## 2887 2 none 0 BB FRA Groups
## 2888 2 none 0 BB IRL Online TA
## 2889 2 none 0 HB DEU Offline TA/TO
## 2890 2 none 0 BB GBR Groups
## 2891 1 none 0 BB IRL Groups
## 2892 2 none 0 BB GBR Groups
## 2893 2 none 0 BB GBR Groups
## 2894 1 none 0 BB GBR Groups
## 2895 2 none 0 BB GBR Groups
## 2896 2 none 0 BB GBR Groups
## 2897 2 none 0 BB GBR Groups
## 2898 2 none 0 BB GBR Groups
## 2899 1 none 0 BB GBR Groups
## 2900 1 none 0 BB GBR Groups
## 2901 2 none 0 BB GBR Groups
## 2902 2 none 0 SC GBR Groups
## 2903 2 none 0 HB DEU Online TA
## 2904 2 none 0 HB DEU Online TA
## 2905 2 none 0 BB GBR Offline TA/TO
## 2906 2 none 0 BB GBR Offline TA/TO
## 2907 1 none 0 BB PRT Groups
## 2908 1 none 0 BB GBR Groups
## 2909 2 none 0 BB IRL Offline TA/TO
## 2910 2 none 0 BB IRL Offline TA/TO
## 2911 2 none 0 BB GBR Online TA
## 2912 2 none 0 HB DEU Groups
## 2913 2 none 0 HB DEU Groups
## 2914 1 none 0 HB DEU Groups
## 2915 1 none 0 HB DEU Groups
## 2916 2 none 0 HB DEU Groups
## 2917 2 none 0 HB DEU Groups
## 2918 1 none 0 HB DEU Groups
## 2919 1 none 0 HB DEU Groups
## 2920 2 none 0 HB DEU Groups
## 2921 2 none 0 HB DEU Groups
## 2922 2 none 0 HB DEU Groups
## 2923 2 none 0 HB DEU Groups
## 2924 2 none 0 HB DEU Groups
## 2925 1 none 0 HB DEU Groups
## 2926 2 none 0 HB DEU Groups
## 2927 2 none 0 HB DEU Groups
## 2928 2 none 0 HB DEU Groups
## 2929 1 none 0 HB PRT Groups
## 2930 1 none 0 HB DEU Groups
## 2931 2 none 0 HB DEU Groups
## 2932 2 none 0 HB NLD Groups
## 2933 2 none 0 HB DEU Groups
## 2934 2 none 0 HB DEU Groups
## 2935 2 none 0 HB DEU Groups
## 2936 2 none 0 HB DEU Groups
## 2937 2 none 0 HB DEU Groups
## 2938 2 none 0 HB DEU Groups
## 2939 2 none 0 HB DEU Groups
## 2940 1 none 0 HB DEU Groups
## 2941 2 none 0 BB PRT Groups
## 2942 2 none 0 BB GBR Offline TA/TO
## 2943 1 none 0 BB IRL Online TA
## 2944 2 none 0 BB IRL Offline TA/TO
## 2945 2 none 0 HB DEU Groups
## 2946 2 none 0 HB DEU Groups
## 2947 2 none 0 HB DEU Groups
## 2948 1 none 0 HB DEU Groups
## 2949 2 none 0 HB DEU Groups
## 2950 2 none 0 HB DEU Groups
## 2951 2 none 0 HB DEU Groups
## 2952 1 none 0 HB DEU Groups
## 2953 2 none 0 HB DEU Groups
## 2954 2 none 0 HB DEU Groups
## 2955 2 none 0 HB DEU Groups
## 2956 2 none 0 HB DEU Groups
## 2957 2 none 0 HB DEU Groups
## 2958 2 none 0 HB DEU Groups
## 2959 2 none 0 HB DEU Groups
## 2960 2 none 0 HB DEU Groups
## 2961 2 none 0 HB DEU Groups
## 2962 1 none 0 HB DEU Groups
## 2963 2 none 0 HB DEU Groups
## 2964 2 none 0 HB DEU Groups
## 2965 1 none 0 HB DEU Groups
## 2966 1 none 0 HB DEU Groups
## 2967 1 none 0 HB PRT Groups
## 2968 2 none 0 HB DEU Groups
## 2969 2 none 0 HB DEU Groups
## 2970 2 none 0 HB DEU Groups
## 2971 1 none 0 HB DEU Groups
## 2972 2 none 0 HB DEU Groups
## 2973 2 none 0 HB DEU Groups
## 2974 2 none 0 HB DEU Groups
## 2975 2 none 0 BB GBR Offline TA/TO
## 2976 2 none 0 BB GBR Offline TA/TO
## 2977 1 none 0 HB POL Offline TA/TO
## 2978 2 none 0 BB GBR Offline TA/TO
## 2979 2 none 0 HB GBR Offline TA/TO
## 2980 2 none 0 BB IRL Offline TA/TO
## 2981 2 none 0 BB DEU Online TA
## 2982 2 none 0 BB DEU Online TA
## 2983 2 none 0 BB DEU Online TA
## 2984 2 none 0 BB DEU Online TA
## 2985 2 none 0 BB DEU Online TA
## 2986 2 none 0 BB PRT Offline TA/TO
## 2987 2 none 0 BB GBR Offline TA/TO
## 2988 2 none 0 BB GBR Offline TA/TO
## 2989 2 none 0 BB GBR Offline TA/TO
## 2990 2 none 0 HB IRL Offline TA/TO
## 2991 2 none 0 BB GBR Offline TA/TO
## 2992 2 none 0 BB NLD Online TA
## 2993 2 none 0 BB PRT Complementary
## 2994 2 none 0 BB GBR Offline TA/TO
## 2995 2 none 0 BB GBR Offline TA/TO
## 2996 2 none 0 BB IRL Offline TA/TO
## 2997 2 none 0 BB GBR Offline TA/TO
## 2998 2 none 0 BB IRL Online TA
## 2999 2 none 0 HB GBR Offline TA/TO
## 3000 3 none 0 BB PRT Direct
## 3001 2 none 0 BB BEL Direct
## 3002 2 none 0 BB IRL Online TA
## 3003 2 none 0 BB GBR Groups
## 3004 2 none 0 HB GBR Offline TA/TO
## 3005 2 none 0 HB GBR Online TA
## 3006 2 none 0 HB GBR Offline TA/TO
## 3007 2 none 0 BB GBR Offline TA/TO
## 3008 2 none 0 BB GBR Groups
## 3009 2 none 0 HB ESP Online TA
## 3010 2 none 0 BB GBR Groups
## 3011 2 none 0 HB IRL Direct
## 3012 2 none 0 HB BEL Direct
## 3013 2 none 0 BB PRT Online TA
## 3014 1 none 0 BB GBR Groups
## 3015 2 none 0 BB GBR Groups
## 3016 2 none 0 BB GBR Groups
## 3017 2 children 1 BB GBR Groups
## 3018 2 none 0 BB GBR Groups
## 3019 2 none 0 BB GBR Groups
## 3020 2 none 0 BB GBR Groups
## 3021 2 none 0 BB GBR Groups
## 3022 2 none 0 BB GBR Groups
## 3023 2 none 0 BB GBR Groups
## 3024 2 none 0 BB GBR Groups
## 3025 1 none 0 BB GBR Groups
## 3026 2 none 0 BB GBR Groups
## 3027 2 none 0 BB GBR Groups
## 3028 2 none 0 BB GBR Groups
## 3029 2 none 0 BB GBR Groups
## 3030 2 none 0 BB GBR Groups
## 3031 2 none 0 BB GBR Groups
## 3032 2 none 0 BB GBR Groups
## 3033 2 none 0 BB IRL Offline TA/TO
## 3034 2 none 0 BB GBR Online TA
## 3035 1 none 0 BB GBR Groups
## 3036 2 none 0 HB IRL Offline TA/TO
## 3037 2 none 0 HB IRL Offline TA/TO
## 3038 2 children 0 BB DEU Online TA
## 3039 2 none 0 BB FRA Groups
## 3040 2 none 0 BB ITA Groups
## 3041 2 none 0 BB GBR Groups
## 3042 2 none 0 BB GBR Groups
## 3043 2 none 0 BB GBR Groups
## 3044 2 none 0 BB GBR Groups
## 3045 2 none 0 BB GBR Groups
## 3046 2 none 0 BB GBR Groups
## 3047 2 none 0 BB GBR Groups
## 3048 1 none 0 BB GBR Groups
## 3049 2 none 0 BB CHE Online TA
## 3050 1 none 0 BB GBR Groups
## 3051 1 none 0 BB GBR Groups
## 3052 1 none 0 BB GBR Groups
## 3053 1 none 0 BB GBR Groups
## 3054 2 none 0 BB GBR Groups
## 3055 2 none 0 BB GBR Groups
## 3056 1 none 0 BB GBR Groups
## 3057 1 none 0 BB GBR Groups
## 3058 3 none 0 BB GBR Groups
## 3059 1 none 0 BB ROU Groups
## 3060 1 none 0 BB GBR Groups
## 3061 1 none 0 BB GBR Groups
## 3062 1 none 0 BB GBR Groups
## 3063 2 none 0 BB GBR Groups
## 3064 2 none 0 BB GBR Groups
## 3065 2 none 0 BB GBR Groups
## 3066 2 none 0 BB GBR Groups
## 3067 1 none 0 BB GBR Groups
## 3068 2 none 0 BB GBR Groups
## 3069 2 none 0 BB GBR Groups
## 3070 1 none 0 BB GBR Groups
## 3071 1 none 0 BB GBR Groups
## 3072 2 none 0 BB GBR Groups
## 3073 2 none 0 BB GBR Groups
## 3074 1 none 0 BB GBR Groups
## 3075 1 none 0 BB GBR Groups
## 3076 2 none 0 BB GBR Groups
## 3077 1 none 0 BB GBR Groups
## 3078 1 none 0 BB GBR Groups
## 3079 2 none 0 BB GBR Groups
## 3080 2 none 0 BB GBR Groups
## 3081 2 none 0 BB GBR Groups
## 3082 1 none 0 BB GBR Groups
## 3083 1 none 0 BB GBR Groups
## 3084 1 none 0 BB GBR Groups
## 3085 1 none 0 BB GBR Groups
## 3086 1 none 0 BB GBR Groups
## 3087 2 children 0 BB JAM Offline TA/TO
## 3088 2 none 0 HB GBR Offline TA/TO
## 3089 2 children 0 BB GBR Offline TA/TO
## 3090 2 children 1 BB GBR Offline TA/TO
## 3091 1 none 0 BB GBR Groups
## 3092 2 none 0 BB IRL Online TA
## 3093 2 none 0 BB GBR Offline TA/TO
## 3094 2 none 0 BB IRL Online TA
## 3095 2 none 0 BB IRL Online TA
## 3096 2 none 0 BB GBR Offline TA/TO
## 3097 2 none 0 HB DEU Groups
## 3098 2 none 0 HB DEU Groups
## 3099 2 none 0 HB DEU Groups
## 3100 2 none 0 HB DEU Groups
## 3101 1 none 0 HB DEU Groups
## 3102 1 none 0 HB DEU Groups
## 3103 1 none 0 HB DEU Groups
## 3104 1 none 0 HB DEU Groups
## 3105 2 none 0 HB DEU Groups
## 3106 2 none 0 HB DEU Groups
## 3107 1 none 0 HB DEU Groups
## 3108 2 none 0 HB DEU Groups
## 3109 1 none 0 HB DEU Groups
## 3110 1 none 0 HB DEU Groups
## 3111 2 none 0 HB DEU Groups
## 3112 2 none 0 BB GBR Offline TA/TO
## 3113 2 none 0 HB DEU Groups
## 3114 2 none 0 HB DEU Groups
## 3115 2 none 0 HB DEU Groups
## 3116 2 none 0 HB GBR Offline TA/TO
## 3117 2 none 0 BB GBR Offline TA/TO
## 3118 1 none 0 BB PRT Groups
## 3119 1 none 0 BB GBR Groups
## 3120 2 none 0 BB PRT Groups
## 3121 2 none 0 BB IRL Online TA
## 3122 2 none 0 BB IRL Online TA
## 3123 2 none 0 BB IRL Online TA
## 3124 1 none 0 HB DEU Offline TA/TO
## distribution_channel is_repeated_guest previous_cancellations
## 1 Direct 0 0
## 2 Direct 0 0
## 3 Direct 0 0
## 4 Corporate 0 0
## 5 TA/TO 0 0
## 6 TA/TO 0 0
## 7 Direct 0 0
## 8 Direct 0 0
## 9 TA/TO 0 0
## 10 TA/TO 0 0
## 11 TA/TO 0 0
## 12 TA/TO 0 0
## 13 TA/TO 0 0
## 14 TA/TO 0 0
## 15 TA/TO 0 0
## 16 Corporate 0 0
## 17 Direct 0 0
## 18 TA/TO 0 0
## 19 Direct 0 0
## 20 Direct 0 0
## 21 Direct 0 0
## 22 TA/TO 0 0
## 23 TA/TO 0 0
## 24 TA/TO 0 0
## 25 TA/TO 0 0
## 26 TA/TO 0 0
## 27 Direct 0 0
## 28 TA/TO 0 0
## 29 TA/TO 0 0
## 30 TA/TO 0 0
## 31 TA/TO 0 0
## 32 Direct 0 0
## 33 TA/TO 0 0
## 34 Direct 0 0
## 35 Direct 0 0
## 36 TA/TO 0 0
## 37 TA/TO 0 0
## 38 TA/TO 0 0
## 39 TA/TO 0 0
## 40 TA/TO 0 0
## 41 TA/TO 0 0
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## 46 Direct 0 0
## 47 Direct 0 0
## 48 TA/TO 0 0
## 49 Direct 0 0
## 50 TA/TO 0 0
## 51 TA/TO 0 0
## 52 TA/TO 0 0
## 53 Direct 0 0
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## 55 TA/TO 0 0
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## 65 TA/TO 0 0
## 66 TA/TO 0 0
## 67 TA/TO 0 0
## 68 TA/TO 0 0
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## 70 TA/TO 0 0
## 71 TA/TO 0 0
## 72 TA/TO 0 0
## 73 TA/TO 0 0
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## 75 TA/TO 0 0
## 76 TA/TO 0 0
## 77 Direct 0 0
## 78 Direct 0 0
## 79 TA/TO 0 0
## 80 TA/TO 0 0
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## 83 TA/TO 0 0
## 84 TA/TO 0 0
## 85 TA/TO 0 0
## 86 TA/TO 0 0
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## 88 TA/TO 0 0
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## 90 TA/TO 0 0
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## 92 Direct 0 0
## 93 TA/TO 0 0
## 94 TA/TO 0 0
## 95 Direct 0 0
## 96 TA/TO 0 0
## 97 TA/TO 0 0
## 98 TA/TO 0 0
## 99 TA/TO 0 0
## 100 TA/TO 0 0
## 101 TA/TO 0 0
## 102 TA/TO 0 0
## 103 TA/TO 0 0
## 104 TA/TO 0 0
## 105 TA/TO 0 0
## 106 TA/TO 0 0
## 107 Direct 0 0
## 108 TA/TO 0 0
## 109 TA/TO 0 0
## 110 TA/TO 0 0
## 111 TA/TO 0 0
## 112 TA/TO 0 0
## 113 TA/TO 0 0
## 114 TA/TO 0 0
## 115 TA/TO 0 0
## 116 Direct 0 0
## 117 TA/TO 0 0
## 118 TA/TO 0 0
## 119 TA/TO 0 0
## 120 TA/TO 0 0
## 121 Direct 0 0
## 122 TA/TO 0 0
## 123 TA/TO 0 0
## 124 TA/TO 0 0
## 125 TA/TO 0 0
## 126 TA/TO 0 0
## 127 TA/TO 0 0
## 128 TA/TO 0 0
## 129 Direct 0 0
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## 131 TA/TO 0 0
## 132 TA/TO 0 0
## 133 Direct 0 0
## 134 Direct 0 0
## 135 TA/TO 0 0
## 136 TA/TO 0 0
## 137 TA/TO 0 0
## 138 TA/TO 0 0
## 139 TA/TO 0 0
## 140 TA/TO 0 0
## 141 Direct 0 0
## 142 TA/TO 0 0
## 143 TA/TO 0 0
## 144 TA/TO 0 0
## 145 TA/TO 0 0
## 146 TA/TO 0 0
## 147 TA/TO 0 0
## 148 TA/TO 0 0
## 149 Direct 0 0
## 150 TA/TO 0 0
## 151 Direct 0 0
## 152 TA/TO 0 0
## 153 TA/TO 0 0
## 154 TA/TO 0 0
## 155 TA/TO 0 0
## 156 Direct 0 0
## 157 Direct 0 0
## 158 TA/TO 0 0
## 159 TA/TO 0 0
## 160 Direct 0 0
## 161 TA/TO 0 0
## 162 TA/TO 0 0
## 163 TA/TO 0 0
## 164 TA/TO 0 0
## 165 Direct 0 0
## 166 TA/TO 0 0
## 167 TA/TO 0 0
## 168 TA/TO 0 0
## 169 TA/TO 0 0
## 170 TA/TO 0 0
## 171 TA/TO 0 0
## 172 TA/TO 0 0
## 173 TA/TO 0 0
## 174 Corporate 0 0
## 175 Direct 0 0
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## 177 TA/TO 0 0
## 178 TA/TO 0 0
## 179 Direct 0 0
## 180 TA/TO 0 0
## 181 TA/TO 0 0
## 182 TA/TO 0 0
## 183 Direct 0 0
## 184 Direct 0 0
## 185 TA/TO 0 0
## 186 Direct 0 0
## 187 TA/TO 0 0
## 188 TA/TO 0 0
## 189 TA/TO 0 0
## 190 TA/TO 0 0
## 191 TA/TO 0 0
## 192 TA/TO 0 0
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## 194 Direct 0 0
## 195 TA/TO 0 0
## 196 TA/TO 0 0
## 197 TA/TO 0 0
## 198 TA/TO 0 0
## 199 TA/TO 0 0
## 200 TA/TO 0 0
## 201 TA/TO 0 0
## 202 TA/TO 0 0
## 203 TA/TO 0 0
## 204 Direct 0 0
## 205 TA/TO 0 0
## 206 Direct 0 0
## 207 Direct 0 0
## 208 TA/TO 0 0
## 209 Direct 0 0
## 210 TA/TO 0 0
## 211 TA/TO 0 0
## 212 Direct 0 0
## 213 TA/TO 0 0
## 214 TA/TO 0 0
## 215 TA/TO 0 0
## 216 TA/TO 0 0
## 217 TA/TO 0 0
## 218 Direct 0 0
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## 220 TA/TO 0 0
## 221 TA/TO 0 0
## 222 TA/TO 0 0
## 223 TA/TO 0 0
## 224 TA/TO 0 0
## 225 TA/TO 0 0
## 226 TA/TO 0 0
## 227 TA/TO 0 0
## 228 TA/TO 0 0
## 229 Direct 0 0
## 230 TA/TO 0 0
## 231 TA/TO 0 0
## 232 TA/TO 0 0
## 233 Direct 0 0
## 234 TA/TO 0 0
## 235 Direct 0 0
## 236 TA/TO 0 0
## 237 Corporate 0 0
## 238 TA/TO 0 0
## 239 TA/TO 0 0
## 240 TA/TO 0 0
## 241 TA/TO 0 0
## 242 TA/TO 0 0
## 243 TA/TO 0 0
## 244 Direct 0 0
## 245 TA/TO 0 0
## 246 Direct 0 0
## 247 TA/TO 0 0
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## 3121 0 A A
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## 3123 0 A A
## 3124 0 A A
## booking_changes deposit_type agent days_in_waiting_list customer_type
## 1 3 No Deposit 14 0 Transient
## 2 4 No Deposit 14 0 Transient
## 3 0 No Deposit 14 0 Transient
## 4 0 No Deposit 304 0 Transient
## 5 0 No Deposit 240 0 Transient
## 6 0 No Deposit 240 0 Transient
## 7 0 No Deposit 14 0 Transient
## 8 0 No Deposit 303 0 Transient
## 9 0 No Deposit 240 0 Transient
## 10 0 No Deposit 240 0 Transient
## 11 1 No Deposit 241 0 Transient
## 12 0 No Deposit 241 0 Transient
## 13 0 No Deposit 240 0 Transient
## 14 0 No Deposit 8 0 Contract
## 15 0 No Deposit 240 0 Transient
## 16 0 No Deposit 14 0 Transient
## 17 0 No Deposit 250 0 Transient
## 18 0 No Deposit 241 0 Transient
## 19 1 No Deposit 250 0 Transient
## 20 1 No Deposit 250 0 Transient
## 21 1 No Deposit 250 0 Transient
## 22 0 No Deposit 115 0 Contract
## 23 0 No Deposit 5 0 Transient
## 24 0 No Deposit 8 0 Contract
## 25 0 No Deposit 240 0 Transient
## 26 0 No Deposit 240 0 Transient
## 27 2 No Deposit 14 0 Transient
## 28 0 No Deposit 241 0 Transient
## 29 0 No Deposit 175 0 Transient
## 30 0 No Deposit 240 0 Transient
## 31 0 No Deposit 241 0 Transient
## 32 0 No Deposit 250 0 Transient
## 33 0 No Deposit 8 0 Contract
## 34 0 No Deposit 14 0 Transient
## 35 0 No Deposit 250 0 Transient
## 36 0 No Deposit 240 0 Transient
## 37 0 No Deposit 134 0 Transient
## 38 0 No Deposit 156 0 Transient-Party
## 39 1 No Deposit 243 0 Contract
## 40 1 No Deposit 241 0 Transient
## 41 0 No Deposit 156 0 Transient-Party
## 42 0 No Deposit 156 0 Contract
## 43 0 No Deposit 243 0 Contract
## 44 0 No Deposit 240 0 Transient
## 45 0 No Deposit 240 0 Transient
## 46 0 No Deposit 14 0 Transient
## 47 0 No Deposit 14 0 Transient
## 48 0 No Deposit 240 0 Transient
## 49 0 No Deposit 14 0 Transient
## 50 0 No Deposit 242 0 Transient
## 51 0 No Deposit 241 0 Transient
## 52 0 No Deposit 240 0 Transient
## 53 0 No Deposit 250 0 Transient
## 54 0 No Deposit 115 0 Contract
## 55 0 No Deposit 240 0 Transient
## 56 0 No Deposit 240 0 Transient
## 57 0 No Deposit 242 0 Transient
## 58 0 No Deposit 3 0 Transient
## 59 0 No Deposit 240 0 Transient
## 60 0 No Deposit 240 0 Transient
## 61 0 No Deposit 240 0 Transient
## 62 0 No Deposit 105 0 Transient
## 63 0 No Deposit 5 0 Transient
## 64 0 No Deposit 40 0 Contract
## 65 0 No Deposit 40 0 Contract
## 66 0 No Deposit 115 0 Contract
## 67 0 No Deposit 40 0 Contract
## 68 1 No Deposit 241 0 Transient
## 69 0 No Deposit 240 0 Transient
## 70 0 No Deposit 240 0 Transient
## 71 0 No Deposit 240 0 Transient
## 72 0 No Deposit 240 0 Transient
## 73 0 No Deposit 241 0 Transient
## 74 0 No Deposit 240 0 Transient
## 75 0 No Deposit 105 0 Transient
## 76 0 No Deposit 240 0 Transient
## 77 0 No Deposit 250 0 Transient
## 78 0 No Deposit 250 0 Transient
## 79 0 No Deposit 8 0 Transient
## 80 0 No Deposit 241 0 Transient
## 81 0 No Deposit 240 0 Transient
## 82 0 No Deposit 240 0 Transient
## 83 0 No Deposit 134 0 Transient
## 84 0 No Deposit 240 0 Transient
## 85 0 No Deposit 240 0 Transient
## 86 0 No Deposit 240 0 Transient
## 87 0 No Deposit 147 0 Transient
## 88 0 No Deposit 241 0 Transient
## 89 0 No Deposit 240 0 Transient
## 90 0 No Deposit 40 0 Contract
## 91 0 No Deposit 40 0 Contract
## 92 1 No Deposit 250 0 Transient
## 93 1 No Deposit 240 0 Transient
## 94 0 No Deposit 8 0 Contract
## 95 2 No Deposit 14 0 Transient
## 96 0 No Deposit 240 0 Transient
## 97 0 No Deposit 240 0 Transient-Party
## 98 0 No Deposit 240 0 Transient
## 99 0 No Deposit 240 0 Transient-Party
## 100 0 No Deposit 240 0 Transient-Party
## 101 0 No Deposit 240 0 Transient
## 102 1 No Deposit 306 0 Transient
## 103 0 No Deposit 184 0 Transient
## 104 0 No Deposit 240 0 Transient
## 105 0 No Deposit 240 0 Transient
## 106 0 No Deposit 184 0 Transient
## 107 0 No Deposit 14 0 Transient
## 108 3 No Deposit 240 0 Transient
## 109 0 No Deposit 240 0 Transient
## 110 0 No Deposit 240 0 Transient
## 111 0 No Deposit 241 0 Transient
## 112 0 No Deposit 240 0 Transient
## 113 0 No Deposit 240 0 Transient
## 114 0 No Deposit 240 0 Transient
## 115 0 No Deposit 240 0 Transient
## 116 1 No Deposit 250 0 Transient
## 117 0 No Deposit 241 0 Transient
## 118 0 No Deposit 175 0 Transient
## 119 1 No Deposit 240 0 Transient
## 120 0 No Deposit 5 0 Transient
## 121 3 No Deposit 14 0 Transient
## 122 0 No Deposit 40 0 Contract
## 123 0 No Deposit 40 0 Contract
## 124 0 No Deposit 243 0 Contract
## 125 1 No Deposit 240 0 Transient
## 126 0 No Deposit 240 0 Transient
## 127 0 No Deposit 241 0 Transient
## 128 0 No Deposit 96 0 Transient
## 129 0 No Deposit 250 0 Transient
## 130 1 No Deposit 240 0 Transient
## 131 0 No Deposit 241 0 Transient
## 132 0 No Deposit 240 0 Transient
## 133 0 No Deposit 250 0 Transient
## 134 0 No Deposit 14 0 Transient
## 135 0 No Deposit 240 0 Transient
## 136 0 No Deposit 240 0 Transient
## 137 0 No Deposit 240 0 Transient
## 138 0 No Deposit 175 0 Transient
## 139 1 No Deposit 240 0 Transient
## 140 0 No Deposit 240 0 Transient
## 141 0 No Deposit 250 0 Transient
## 142 0 No Deposit 240 0 Transient
## 143 1 No Deposit 240 0 Transient
## 144 0 No Deposit 240 0 Transient
## 145 0 No Deposit 2 0 Contract
## 146 0 No Deposit 240 0 Transient
## 147 0 No Deposit 242 0 Transient
## 148 1 No Deposit 241 0 Transient
## 149 5 No Deposit 250 0 Transient
## 150 0 No Deposit 156 0 Contract
## 151 1 No Deposit 14 0 Transient
## 152 0 No Deposit 8 0 Contract
## 153 0 No Deposit 40 0 Contract
## 154 0 No Deposit 127 0 Transient
## 155 0 No Deposit 240 0 Transient
## 156 0 No Deposit 250 0 Transient
## 157 0 No Deposit 14 0 Transient
## 158 0 No Deposit 240 0 Transient
## 159 0 No Deposit 240 0 Transient
## 160 0 No Deposit 14 0 Transient
## 161 0 No Deposit 240 0 Transient
## 162 2 No Deposit 240 0 Transient
## 163 0 No Deposit 240 0 Transient
## 164 0 No Deposit 95 0 Transient
## 165 2 No Deposit 14 0 Transient
## 166 0 No Deposit 8 0 Transient
## 167 0 No Deposit 156 0 Contract
## 168 0 No Deposit 240 0 Transient
## 169 0 No Deposit 240 0 Transient
## 170 0 No Deposit 240 0 Transient
## 171 0 No Deposit 240 0 Transient
## 172 0 No Deposit 105 0 Transient
## 173 0 No Deposit 146 0 Transient
## 174 0 No Deposit 14 0 Transient
## 175 1 No Deposit 250 0 Transient
## 176 0 No Deposit 240 0 Transient
## 177 0 No Deposit 40 0 Contract
## 178 0 No Deposit 240 0 Transient
## 179 0 No Deposit 250 0 Transient
## 180 1 No Deposit 240 0 Transient-Party
## 181 0 No Deposit 240 0 Transient-Party
## 182 0 No Deposit 242 0 Transient
## 183 0 No Deposit 250 0 Transient
## 184 2 No Deposit 14 0 Transient
## 185 0 No Deposit 240 0 Transient
## 186 0 No Deposit 250 0 Transient
## 187 0 No Deposit 241 0 Transient
## 188 0 No Deposit 177 0 Transient
## 189 0 No Deposit 6 0 Transient
## 190 0 No Deposit 40 0 Contract
## 191 0 No Deposit 143 0 Contract
## 192 0 No Deposit 40 0 Contract
## 193 0 No Deposit 240 0 Transient
## 194 0 No Deposit 250 0 Transient
## 195 0 No Deposit 240 0 Transient
## 196 0 No Deposit 240 0 Transient
## 197 0 No Deposit 240 0 Transient
## 198 0 No Deposit 240 0 Transient
## 199 0 No Deposit 240 0 Transient
## 200 2 No Deposit 240 0 Transient
## 201 1 No Deposit 240 0 Transient
## 202 0 No Deposit 240 0 Transient
## 203 0 No Deposit 105 0 Transient
## 204 0 No Deposit 250 0 Transient
## 205 0 No Deposit 240 0 Transient
## 206 0 No Deposit 14 0 Transient-Party
## 207 0 No Deposit 14 0 Transient-Party
## 208 0 No Deposit 240 0 Transient
## 209 1 No Deposit 250 0 Transient
## 210 0 No Deposit 240 0 Transient
## 211 1 No Deposit 240 0 Transient
## 212 1 No Deposit 250 0 Transient
## 213 1 No Deposit 240 0 Transient
## 214 1 No Deposit 240 0 Transient
## 215 2 No Deposit 244 0 Transient
## 216 0 No Deposit 241 0 Transient
## 217 0 No Deposit 5 0 Transient
## 218 0 No Deposit 250 0 Transient
## 219 0 No Deposit 240 0 Transient
## 220 0 No Deposit 240 0 Transient
## 221 0 No Deposit 241 0 Transient
## 222 0 No Deposit 241 0 Transient
## 223 0 No Deposit 241 0 Transient
## 224 0 No Deposit 241 0 Transient
## 225 0 No Deposit 240 0 Transient
## 226 1 No Deposit 240 0 Transient
## 227 0 No Deposit 15 0 Transient
## 228 0 No Deposit 240 0 Transient
## 229 0 No Deposit 250 0 Transient
## 230 0 No Deposit 241 0 Transient
## 231 0 No Deposit 240 0 Transient
## 232 1 No Deposit 240 0 Transient
## 233 0 No Deposit 14 0 Transient
## 234 1 No Deposit 240 0 Transient
## 235 0 No Deposit 14 0 Transient
## 236 0 No Deposit 240 0 Transient
## 237 0 No Deposit 14 0 Transient
## 238 0 No Deposit 175 0 Transient
## 239 0 No Deposit 242 0 Transient
## 240 0 No Deposit 240 0 Transient
## 241 0 No Deposit 240 0 Transient
## 242 0 No Deposit 5 0 Transient
## 243 0 No Deposit 242 0 Transient
## 244 1 No Deposit 250 0 Transient
## 245 1 No Deposit 240 0 Transient
## 246 0 No Deposit 250 0 Transient
## 247 0 No Deposit 240 0 Transient
## 248 0 No Deposit 240 0 Transient
## 249 0 No Deposit 240 0 Transient
## 250 0 No Deposit 240 0 Transient
## 251 0 No Deposit 240 0 Transient
## 252 0 No Deposit 243 0 Transient
## 253 0 No Deposit 5 0 Transient-Party
## 254 1 No Deposit 156 0 Transient
## 255 0 No Deposit 115 0 Contract
## 256 0 No Deposit 5 0 Transient-Party
## 257 0 No Deposit 40 0 Contract
## 258 0 No Deposit 243 0 Transient
## 259 0 No Deposit 243 0 Transient
## 260 0 No Deposit 240 0 Transient
## 261 0 No Deposit 240 0 Transient
## 262 0 No Deposit 240 0 Transient
## 263 0 No Deposit 243 0 Transient
## 264 0 No Deposit 243 0 Contract
## 265 0 No Deposit 147 0 Transient
## 266 0 No Deposit 240 0 Transient
## 267 0 No Deposit 240 0 Transient
## 268 0 No Deposit 240 0 Transient
## 269 0 No Deposit 241 0 Transient
## 270 0 No Deposit 240 0 Transient
## 271 0 No Deposit 240 0 Transient
## 272 1 No Deposit 8 0 Contract
## 273 0 No Deposit 240 0 Transient
## 274 0 No Deposit 6 0 Transient
## 275 2 No Deposit 240 0 Transient
## 276 0 No Deposit 250 0 Transient
## 277 0 No Deposit 156 0 Contract
## 278 0 No Deposit 240 0 Transient
## 279 0 No Deposit 240 0 Transient
## 280 0 No Deposit 240 0 Transient
## 281 1 No Deposit 134 0 Transient-Party
## 282 0 No Deposit 240 0 Transient
## 283 0 No Deposit 240 0 Transient-Party
## 284 0 No Deposit 240 0 Transient
## 285 0 No Deposit 240 0 Transient
## 286 0 No Deposit 240 0 Transient-Party
## 287 0 No Deposit 14 0 Transient
## 288 0 No Deposit 240 0 Transient
## 289 0 No Deposit 147 0 Transient
## 290 0 No Deposit 240 0 Transient
## 291 0 No Deposit 240 0 Transient
## 292 0 No Deposit 240 0 Transient
## 293 0 No Deposit 240 0 Transient
## 294 2 No Deposit 250 0 Transient
## 295 0 No Deposit 167 0 Transient
## 296 0 No Deposit 250 0 Transient
## 297 0 No Deposit 14 0 Transient
## 298 0 No Deposit 240 0 Transient
## 299 0 No Deposit 250 0 Transient
## 300 0 No Deposit 14 0 Transient
## 301 2 No Deposit 300 0 Transient
## 302 0 No Deposit 240 0 Transient
## 303 0 No Deposit 134 0 Transient-Party
## 304 0 No Deposit 250 0 Transient
## 305 0 No Deposit 250 0 Transient
## 306 0 No Deposit 240 0 Transient
## 307 1 No Deposit 240 0 Transient
## 308 0 No Deposit 8 0 Contract
## 309 0 No Deposit 147 0 Transient
## 310 0 No Deposit 14 0 Transient
## 311 2 No Deposit 240 0 Transient
## 312 1 No Deposit 240 0 Transient
## 313 0 No Deposit 240 0 Transient
## 314 1 No Deposit 240 0 Transient
## 315 0 No Deposit 250 0 Transient
## 316 0 No Deposit 14 0 Transient-Party
## 317 0 No Deposit 14 0 Transient-Party
## 318 1 No Deposit 240 0 Transient
## 319 0 No Deposit 240 0 Transient
## 320 1 No Deposit 240 0 Transient
## 321 0 No Deposit 14 0 Transient
## 322 0 No Deposit 147 0 Transient
## 323 0 No Deposit 241 0 Transient
## 324 1 No Deposit 241 0 Transient
## 325 1 No Deposit 14 0 Transient
## 326 0 No Deposit 14 0 Transient
## 327 0 No Deposit 241 0 Transient
## 328 0 No Deposit 240 0 Transient
## 329 0 No Deposit 305 0 Transient-Party
## 330 0 No Deposit 305 0 Transient-Party
## 331 0 No Deposit 305 0 Transient-Party
## 332 0 No Deposit 305 0 Transient-Party
## 333 0 No Deposit 305 0 Transient
## 334 0 No Deposit 240 0 Transient
## 335 0 No Deposit 305 0 Transient
## 336 1 No Deposit 305 0 Transient-Party
## 337 0 No Deposit 305 0 Transient-Party
## 338 1 No Deposit 305 0 Transient-Party
## 339 1 No Deposit 305 0 Transient-Party
## 340 0 No Deposit 305 0 Transient
## 341 0 No Deposit 305 0 Transient
## 342 1 No Deposit 305 0 Transient-Party
## 343 1 No Deposit 305 0 Transient-Party
## 344 0 No Deposit 305 0 Transient-Party
## 345 0 No Deposit 305 0 Transient
## 346 0 No Deposit 305 0 Transient
## 347 0 No Deposit 305 0 Transient-Party
## 348 0 No Deposit 305 0 Transient-Party
## 349 0 No Deposit 240 0 Transient
## 350 0 No Deposit 241 0 Transient
## 351 1 No Deposit 242 0 Transient
## 352 1 No Deposit 14 0 Transient
## 353 0 No Deposit 240 0 Transient
## 354 0 No Deposit 240 0 Transient
## 355 0 No Deposit 250 0 Transient
## 356 0 No Deposit 240 0 Transient
## 357 0 No Deposit 250 0 Transient-Party
## 358 1 No Deposit 250 0 Transient-Party
## 359 0 No Deposit 147 0 Transient
## 360 0 No Deposit 240 0 Transient
## 361 0 No Deposit 243 0 Contract
## 362 1 No Deposit 67 0 Transient
## 363 1 No Deposit 14 0 Transient
## 364 0 No Deposit 240 0 Transient
## 365 0 No Deposit 14 0 Transient
## 366 0 No Deposit 240 0 Transient
## 367 0 No Deposit 240 0 Transient
## 368 0 No Deposit 196 0 Transient
## 369 0 No Deposit 240 0 Transient
## 370 0 No Deposit 14 0 Transient
## 371 0 No Deposit 250 0 Transient
## 372 0 No Deposit 196 0 Transient
## 373 0 No Deposit 240 0 Transient
## 374 0 No Deposit 152 0 Transient-Party
## 375 0 No Deposit 152 0 Transient-Party
## 376 0 No Deposit 250 0 Transient
## 377 0 No Deposit 175 0 Transient-Party
## 378 0 No Deposit 152 0 Transient-Party
## 379 0 No Deposit 152 0 Transient-Party
## 380 0 No Deposit 175 0 Transient-Party
## 381 0 No Deposit 152 0 Transient-Party
## 382 1 No Deposit 14 0 Transient
## 383 0 No Deposit 240 0 Transient
## 384 0 No Deposit 250 0 Transient
## 385 1 No Deposit 240 0 Transient
## 386 1 No Deposit 240 0 Transient
## 387 0 No Deposit 240 0 Transient
## 388 0 No Deposit 142 0 Transient
## 389 1 No Deposit 240 0 Transient
## 390 0 No Deposit 240 0 Transient
## 391 0 No Deposit 240 0 Transient
## 392 0 No Deposit 250 0 Transient
## 393 1 No Deposit 14 0 Transient
## 394 3 No Deposit 240 0 Transient
## 395 3 No Deposit 240 0 Transient
## 396 1 No Deposit 240 0 Transient
## 397 0 No Deposit 142 0 Contract
## 398 0 No Deposit 240 0 Transient
## 399 0 No Deposit 240 0 Transient
## 400 0 No Deposit 171 0 Transient
## 401 0 No Deposit 240 0 Transient
## 402 4 No Deposit 240 0 Transient
## 403 0 No Deposit 240 0 Transient
## 404 0 No Deposit 14 0 Transient
## 405 0 No Deposit 240 0 Transient
## 406 0 No Deposit 250 0 Transient
## 407 1 No Deposit 240 0 Transient
## 408 0 No Deposit 240 0 Transient
## 409 0 No Deposit 14 0 Transient
## 410 1 No Deposit 250 0 Transient
## 411 0 No Deposit 240 0 Transient
## 412 0 No Deposit 8 0 Contract
## 413 0 No Deposit 241 0 Transient
## 414 0 No Deposit 14 0 Transient
## 415 0 No Deposit 14 0 Transient
## 416 0 No Deposit 14 0 Transient
## 417 0 No Deposit 240 0 Transient
## 418 0 No Deposit 14 0 Transient
## 419 0 No Deposit 14 0 Transient
## 420 0 No Deposit 240 0 Transient
## 421 0 No Deposit 5 0 Transient
## 422 0 No Deposit 250 0 Transient
## 423 0 No Deposit 14 0 Transient
## 424 0 No Deposit 14 0 Transient
## 425 0 No Deposit 250 0 Transient
## 426 0 No Deposit 196 0 Transient
## 427 0 No Deposit 196 0 Transient
## 428 0 No Deposit 240 0 Transient
## 429 0 No Deposit 14 0 Transient
## 430 0 No Deposit 240 0 Group
## 431 0 No Deposit 250 0 Transient
## 432 0 No Deposit 14 0 Group
## 433 0 No Deposit 240 0 Transient
## 434 0 No Deposit 240 0 Transient
## 435 0 No Deposit 14 0 Transient
## 436 0 No Deposit 14 0 Transient
## 437 0 No Deposit 241 0 Transient
## 438 0 No Deposit 115 0 Contract
## 439 1 No Deposit 240 0 Transient
## 440 0 No Deposit 156 0 Transient
## 441 0 No Deposit 241 0 Transient
## 442 0 No Deposit 115 0 Contract
## 443 0 No Deposit 156 0 Transient
## 444 0 No Deposit 142 0 Contract
## 445 0 No Deposit 243 0 Transient
## 446 2 No Deposit 240 0 Transient
## 447 0 No Deposit 104 0 Transient
## 448 0 No Deposit 5 0 Transient
## 449 0 No Deposit 241 0 Transient
## 450 0 No Deposit 240 0 Transient
## 451 0 No Deposit 240 0 Transient
## 452 3 No Deposit 240 0 Transient
## 453 0 No Deposit 250 0 Transient
## 454 0 No Deposit 241 0 Transient
## 455 0 No Deposit 175 0 Transient
## 456 0 No Deposit 250 0 Transient
## 457 0 No Deposit 241 0 Transient
## 458 0 No Deposit 105 0 Transient
## 459 0 No Deposit 240 0 Transient
## 460 0 No Deposit 175 0 Transient
## 461 1 No Deposit 240 0 Transient
## 462 0 No Deposit 15 0 Transient
## 463 1 No Deposit 142 0 Transient
## 464 0 No Deposit 142 0 Transient
## 465 0 No Deposit 142 0 Transient
## 466 0 No Deposit 14 0 Transient
## 467 0 No Deposit 240 0 Transient
## 468 1 No Deposit 250 0 Transient
## 469 0 No Deposit 250 0 Transient
## 470 0 No Deposit 240 0 Transient
## 471 0 No Deposit 6 0 Transient
## 472 0 No Deposit 240 0 Transient
## 473 0 No Deposit 250 0 Transient
## 474 0 No Deposit 241 0 Transient
## 475 0 No Deposit 14 0 Transient
## 476 1 No Deposit 242 0 Transient
## 477 0 No Deposit 242 0 Transient
## 478 1 No Deposit 240 0 Transient
## 479 0 No Deposit 240 0 Transient
## 480 0 No Deposit 250 0 Transient
## 481 1 No Deposit 240 0 Transient-Party
## 482 0 No Deposit 240 0 Transient
## 483 0 No Deposit 241 0 Transient
## 484 1 No Deposit 171 0 Transient
## 485 0 No Deposit 240 0 Transient-Party
## 486 0 No Deposit 240 0 Transient-Party
## 487 0 No Deposit 240 0 Transient-Party
## 488 0 No Deposit 240 0 Transient-Party
## 489 0 No Deposit 240 0 Transient-Party
## 490 0 No Deposit 240 0 Transient
## 491 0 No Deposit 240 0 Transient-Party
## 492 0 No Deposit 250 0 Transient
## 493 0 No Deposit 240 0 Transient-Party
## 494 2 No Deposit 240 0 Transient
## 495 0 No Deposit 134 0 Transient
## 496 0 No Deposit 240 0 Transient
## 497 0 No Deposit 177 0 Transient
## 498 0 No Deposit 147 0 Transient-Party
## 499 0 No Deposit 147 0 Transient-Party
## 500 1 No Deposit 250 0 Transient
## 501 0 No Deposit 240 0 Transient
## 502 0 No Deposit 240 0 Transient
## 503 0 No Deposit 95 0 Transient
## 504 0 No Deposit 241 0 Transient
## 505 0 No Deposit 261 0 Transient
## 506 0 No Deposit 134 0 Transient
## 507 0 No Deposit 241 0 Transient
## 508 0 No Deposit 134 0 Transient
## 509 0 No Deposit 14 0 Transient
## 510 0 No Deposit 240 0 Transient
## 511 0 No Deposit 142 0 Transient
## 512 1 No Deposit 240 0 Transient
## 513 0 No Deposit 196 0 Transient
## 514 0 No Deposit 241 0 Transient
## 515 0 No Deposit 240 0 Transient
## 516 0 No Deposit 241 0 Transient
## 517 1 No Deposit 14 0 Transient
## 518 0 No Deposit 250 0 Transient
## 519 0 No Deposit 242 0 Transient
## 520 0 No Deposit 240 0 Transient
## 521 0 No Deposit 242 0 Transient
## 522 1 No Deposit 240 0 Transient
## 523 0 No Deposit 142 0 Transient
## 524 0 No Deposit 105 0 Transient
## 525 1 No Deposit 147 0 Transient
## 526 0 No Deposit 240 0 Transient
## 527 0 No Deposit 8 0 Contract
## 528 0 No Deposit 241 0 Transient
## 529 0 No Deposit 14 0 Transient
## 530 0 No Deposit 240 0 Transient
## 531 0 No Deposit 240 0 Transient-Party
## 532 0 No Deposit 250 0 Transient
## 533 0 No Deposit 250 0 Transient
## 534 0 No Deposit 241 0 Transient
## 535 0 No Deposit 250 0 Transient
## 536 0 No Deposit 142 0 Transient
## 537 1 No Deposit 241 0 Transient
## 538 0 No Deposit 240 0 Transient
## 539 0 No Deposit 240 0 Transient
## 540 0 No Deposit 240 0 Transient
## 541 0 No Deposit 250 0 Transient
## 542 0 No Deposit 240 0 Transient
## 543 5 No Deposit 14 0 Transient
## 544 0 No Deposit 240 0 Transient
## 545 0 No Deposit 142 0 Transient
## 546 0 No Deposit 240 0 Transient
## 547 0 No Deposit 196 0 Transient
## 548 0 No Deposit 14 0 Transient
## 549 0 No Deposit 250 0 Transient
## 550 0 No Deposit 36 0 Transient
## 551 1 No Deposit 240 0 Transient
## 552 0 No Deposit 196 0 Transient
## 553 0 No Deposit 240 0 Transient
## 554 1 No Deposit 240 0 Transient
## 555 0 No Deposit 5 0 Transient
## 556 0 No Deposit 240 0 Transient
## 557 0 No Deposit 240 0 Transient
## 558 0 No Deposit 142 0 Transient
## 559 0 No Deposit 241 0 Transient
## 560 0 No Deposit 241 0 Transient
## 561 0 No Deposit 241 0 Transient
## 562 0 No Deposit 240 0 Transient
## 563 0 No Deposit 177 0 Transient
## 564 0 No Deposit 171 0 Transient
## 565 0 No Deposit 14 0 Transient
## 566 0 No Deposit 240 0 Transient
## 567 0 No Deposit 250 0 Transient
## 568 0 No Deposit 241 0 Transient
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## 572 0 No Deposit 240 0 Transient
## 573 0 No Deposit 250 0 Transient
## 574 0 No Deposit 14 0 Transient
## 575 0 No Deposit 250 0 Transient
## 576 0 No Deposit 240 0 Transient
## 577 0 No Deposit 6 0 Transient
## 578 0 No Deposit 241 0 Transient
## 579 1 No Deposit 134 0 Transient
## 580 0 No Deposit 240 0 Transient
## 581 0 No Deposit 14 0 Transient
## 582 0 No Deposit 196 0 Transient
## 583 0 No Deposit 240 0 Transient
## 584 2 No Deposit 14 0 Transient
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## 589 0 No Deposit 14 0 Transient
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## 595 0 No Deposit 2 0 Transient
## 596 0 No Deposit 240 0 Transient
## 597 1 No Deposit 105 0 Transient
## 598 0 No Deposit 240 0 Transient
## 599 0 No Deposit 40 0 Contract
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## 601 0 No Deposit 14 0 Transient
## 602 0 No Deposit 240 0 Transient
## 603 1 No Deposit 14 0 Transient
## 604 2 No Deposit 240 0 Transient
## 605 0 No Deposit 104 0 Transient
## 606 0 No Deposit 5 0 Transient
## 607 4 No Deposit 14 0 Transient
## 608 0 No Deposit 14 0 Transient
## 609 0 No Deposit 14 0 Transient
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## 611 0 No Deposit 171 0 Transient
## 612 0 No Deposit 175 0 Transient
## 613 0 No Deposit 250 0 Transient
## 614 0 No Deposit 241 0 Transient
## 615 0 No Deposit 240 0 Transient
## 616 0 No Deposit 40 0 Contract
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## 618 0 No Deposit 240 0 Transient
## 619 0 No Deposit 250 0 Transient
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## 624 0 No Deposit 240 0 Transient
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## 627 0 No Deposit 240 0 Transient
## 628 0 No Deposit 261 0 Transient
## 629 0 No Deposit 240 0 Transient
## 630 0 No Deposit 14 0 Transient
## 631 0 No Deposit 240 0 Transient
## 632 0 No Deposit 240 0 Transient
## 633 1 No Deposit 240 0 Transient
## 634 1 No Deposit 240 0 Transient
## 635 0 No Deposit 240 0 Transient
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## 638 1 No Deposit 250 0 Transient
## 639 1 No Deposit 142 0 Transient
## 640 1 No Deposit 240 0 Transient
## 641 0 No Deposit 240 0 Transient
## 642 2 No Deposit 240 0 Transient
## 643 0 No Deposit 240 0 Transient
## 644 1 No Deposit 250 0 Transient
## 645 0 No Deposit 240 0 Transient
## 646 0 No Deposit 14 0 Transient
## 647 1 No Deposit 240 0 Transient
## 648 0 No Deposit 240 0 Transient
## 649 0 No Deposit 240 0 Transient
## 650 0 No Deposit 250 0 Transient
## 651 0 No Deposit 240 0 Transient
## 652 0 No Deposit 175 0 Transient-Party
## 653 0 No Deposit 240 0 Transient
## 654 0 No Deposit 5 0 Transient
## 655 0 No Deposit 240 0 Transient
## 656 1 No Deposit 240 0 Transient
## 657 0 No Deposit 240 0 Transient
## 658 0 No Deposit 240 0 Transient
## 659 0 No Deposit 240 0 Transient
## 660 0 No Deposit 240 0 Transient
## 661 0 No Deposit 241 0 Transient
## 662 0 No Deposit 240 0 Transient
## 663 0 No Deposit 240 0 Transient
## 664 0 No Deposit 14 0 Transient
## 665 0 No Deposit 250 0 Transient
## 666 0 No Deposit 261 0 Transient
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## 669 0 No Deposit 240 0 Transient
## 670 0 No Deposit 14 0 Transient
## 671 0 No Deposit 306 0 Transient
## 672 0 No Deposit 240 0 Transient
## 673 1 No Deposit 240 0 Transient
## 674 0 No Deposit 240 0 Transient
## 675 0 No Deposit 240 0 Transient
## 676 0 No Deposit 242 0 Transient
## 677 0 No Deposit 250 0 Transient
## 678 0 No Deposit 243 0 Transient
## 679 0 No Deposit 149 0 Transient
## 680 0 No Deposit 243 0 Transient
## 681 0 No Deposit 26 0 Transient
## 682 0 No Deposit 240 0 Transient
## 683 0 No Deposit 96 0 Transient
## 684 0 No Deposit 240 0 Transient
## 685 0 No Deposit 240 0 Transient
## 686 0 No Deposit 258 0 Transient
## 687 0 No Deposit 241 0 Transient
## 688 0 No Deposit 240 0 Transient
## 689 0 No Deposit 241 0 Transient
## 690 0 No Deposit 240 0 Transient
## 691 0 No Deposit 14 0 Transient
## 692 0 No Deposit 240 0 Transient
## 693 0 No Deposit 240 0 Transient
## 694 0 No Deposit 240 0 Transient
## 695 0 No Deposit 240 0 Transient
## 696 0 No Deposit 240 0 Transient
## 697 0 No Deposit 240 0 Transient
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## 699 1 No Deposit 171 0 Transient
## 700 1 No Deposit 241 0 Transient
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## 702 0 No Deposit 241 0 Transient
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## 705 0 No Deposit 240 0 Transient
## 706 0 No Deposit 240 0 Transient
## 707 0 No Deposit 110 0 Transient
## 708 0 No Deposit 14 0 Transient
## 709 0 No Deposit 142 0 Contract
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## 711 0 No Deposit 240 0 Transient
## 712 0 No Deposit 147 0 Transient
## 713 1 No Deposit 240 0 Transient
## 714 0 No Deposit 242 0 Transient
## 715 0 No Deposit 14 0 Transient-Party
## 716 0 No Deposit 242 0 Transient
## 717 0 No Deposit 240 0 Transient
## 718 0 No Deposit 14 0 Transient
## 719 1 No Deposit 14 0 Transient
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## 721 0 No Deposit 240 0 Transient
## 722 0 No Deposit 240 0 Transient-Party
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## 724 0 No Deposit 240 0 Transient
## 725 0 No Deposit 242 0 Transient
## 726 0 No Deposit 240 0 Transient
## 727 0 No Deposit 14 0 Transient
## 728 0 No Deposit 240 0 Transient
## 729 0 No Deposit 240 0 Transient
## 730 2 No Deposit 14 0 Transient
## 731 0 No Deposit 71 0 Transient
## 732 0 No Deposit 71 0 Transient
## 733 0 No Deposit 250 0 Transient
## 734 0 No Deposit 146 0 Transient
## 735 0 No Deposit 250 0 Transient
## 736 0 No Deposit 240 0 Transient
## 737 0 No Deposit 146 0 Transient
## 738 0 No Deposit 175 0 Transient
## 739 0 No Deposit 96 0 Transient-Party
## 740 0 No Deposit 14 0 Transient-Party
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## 742 0 No Deposit 14 0 Transient-Party
## 743 1 No Deposit 67 0 Transient
## 744 0 No Deposit 8 0 Transient
## 745 0 No Deposit 67 0 Transient
## 746 1 No Deposit 14 0 Transient
## 747 0 No Deposit 156 0 Transient
## 748 0 No Deposit 261 0 Transient
## 749 0 No Deposit 14 0 Transient-Party
## 750 0 No Deposit 14 0 Transient
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## 761 0 No Deposit 26 0 Transient
## 762 0 No Deposit 40 0 Transient
## 763 0 No Deposit 134 0 Transient
## 764 0 No Deposit 171 0 Transient
## 765 0 No Deposit 250 0 Transient
## 766 2 No Deposit 240 0 Transient
## 767 1 No Deposit 242 0 Transient
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## 769 0 No Deposit 5 0 Transient
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## 773 1 No Deposit 242 0 Transient
## 774 0 No Deposit 14 0 Transient
## 775 0 No Deposit 240 0 Transient
## 776 1 No Deposit 240 0 Transient
## 777 0 No Deposit 242 0 Transient
## 778 3 No Deposit 240 0 Transient
## 779 1 No Deposit 240 0 Transient
## 780 1 No Deposit 250 0 Transient
## 781 0 No Deposit 240 0 Transient
## 782 0 No Deposit 14 0 Transient
## 783 0 No Deposit 147 0 Transient
## 784 1 No Deposit 240 0 Transient
## 785 0 No Deposit 240 0 Transient
## 786 0 No Deposit 240 0 Transient
## 787 1 No Deposit 14 0 Transient
## 788 0 No Deposit 156 0 Transient
## 789 4 No Deposit 142 0 Transient
## 790 0 No Deposit 240 0 Transient
## 791 0 No Deposit 250 0 Transient
## 792 0 No Deposit 240 0 Transient
## 793 0 No Deposit 240 0 Transient
## 794 0 No Deposit 240 0 Transient
## 795 0 No Deposit 240 0 Transient
## 796 0 No Deposit 240 0 Transient
## 797 0 No Deposit 156 0 Transient
## 798 1 No Deposit 240 0 Transient
## 799 2 No Deposit 240 0 Transient
## 800 0 No Deposit 250 0 Transient
## 801 1 No Deposit 240 0 Transient
## 802 0 No Deposit 14 0 Transient
## 803 0 No Deposit 240 0 Transient
## 804 0 No Deposit 14 0 Transient
## 805 1 No Deposit 240 0 Transient
## 806 1 No Deposit 240 0 Transient
## 807 0 No Deposit 240 0 Transient
## 808 0 No Deposit 240 0 Transient
## 809 0 No Deposit 156 0 Transient
## 810 0 No Deposit 241 0 Transient
## 811 0 No Deposit 14 0 Transient
## 812 0 No Deposit 250 0 Transient
## 813 0 No Deposit 261 0 Transient
## 814 0 No Deposit 261 0 Transient
## 815 0 No Deposit 14 0 Transient
## 816 0 No Deposit 142 0 Contract
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## 818 0 No Deposit 261 0 Transient
## 819 0 No Deposit 5 0 Transient
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## 821 0 No Deposit 261 0 Transient
## 822 0 No Deposit 14 0 Transient
## 823 1 No Deposit 14 0 Transient
## 824 1 No Deposit 181 0 Transient-Party
## 825 2 No Deposit 181 0 Transient-Party
## 826 1 No Deposit 250 0 Transient
## 827 1 No Deposit 261 0 Transient
## 828 1 No Deposit 250 0 Transient
## 829 1 No Deposit 240 0 Transient
## 830 0 No Deposit 250 0 Transient
## 831 0 No Deposit 240 0 Transient
## 832 0 No Deposit 250 0 Transient
## 833 0 No Deposit 240 0 Transient
## 834 0 No Deposit 142 0 Contract
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## 836 0 No Deposit 156 0 Transient
## 837 1 No Deposit 241 0 Transient
## 838 0 No Deposit 14 0 Transient
## 839 0 No Deposit 240 0 Transient
## 840 0 No Deposit 250 0 Transient
## 841 0 No Deposit 14 0 Transient
## 842 0 No Deposit 240 0 Transient
## 843 0 No Deposit 240 0 Transient
## 844 2 No Deposit 240 0 Transient
## 845 1 No Deposit 250 0 Transient
## 846 0 No Deposit 14 0 Transient
## 847 0 No Deposit 14 0 Transient
## 848 0 No Deposit 175 0 Transient
## 849 0 No Deposit 240 0 Transient
## 850 0 No Deposit 240 0 Transient
## 851 0 No Deposit 171 0 Transient
## 852 0 No Deposit 14 0 Transient
## 853 0 No Deposit 181 0 Transient-Party
## 854 0 No Deposit 14 0 Transient
## 855 0 No Deposit 156 0 Transient
## 856 0 No Deposit 14 0 Transient
## 857 1 No Deposit 240 0 Transient
## 858 0 No Deposit 240 0 Transient
## 859 1 No Deposit 240 0 Transient
## 860 0 No Deposit 134 0 Transient
## 861 0 No Deposit 156 0 Contract
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## 863 0 No Deposit 156 0 Contract
## 864 0 No Deposit 156 0 Contract
## 865 0 No Deposit 250 0 Transient
## 866 0 No Deposit 250 0 Transient
## 867 0 No Deposit 14 0 Transient
## 868 1 No Deposit 250 0 Transient
## 869 0 No Deposit 240 0 Transient
## 870 1 No Deposit 242 0 Transient
## 871 1 No Deposit 250 0 Transient
## 872 0 No Deposit 250 0 Transient
## 873 0 No Deposit 240 0 Transient
## 874 1 No Deposit 14 0 Transient
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## 878 0 No Deposit 241 0 Transient
## 879 1 No Deposit 240 0 Transient
## 880 0 No Deposit 240 0 Transient
## 881 0 No Deposit 240 0 Transient
## 882 1 No Deposit 250 0 Transient
## 883 0 No Deposit 250 0 Transient
## 884 0 No Deposit 240 0 Transient
## 885 0 No Deposit 240 0 Transient
## 886 0 No Deposit 240 0 Transient
## 887 0 No Deposit 142 0 Contract
## 888 0 No Deposit 240 0 Transient
## 889 0 No Deposit 250 0 Transient
## 890 0 No Deposit 147 0 Transient
## 891 0 No Deposit 40 0 Transient
## 892 0 No Deposit 250 0 Transient
## 893 0 No Deposit 240 0 Transient
## 894 0 No Deposit 240 0 Transient
## 895 0 No Deposit 241 0 Transient
## 896 0 No Deposit 241 0 Transient
## 897 0 No Deposit 240 0 Transient
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## 900 1 No Deposit 240 0 Transient
## 901 0 No Deposit 14 0 Transient
## 902 5 No Deposit 14 0 Transient
## 903 0 No Deposit 240 0 Transient
## 904 0 No Deposit 14 0 Transient
## 905 1 No Deposit 240 0 Transient
## 906 0 No Deposit 240 0 Transient
## 907 0 No Deposit 240 0 Transient
## 908 0 No Deposit 147 0 Transient
## 909 0 No Deposit 142 0 Contract
## 910 0 No Deposit 241 0 Transient
## 911 0 No Deposit 250 0 Transient
## 912 0 No Deposit 240 0 Transient
## 913 0 No Deposit 240 0 Transient
## 914 0 No Deposit 240 0 Transient
## 915 0 No Deposit 241 0 Transient
## 916 0 No Deposit 241 0 Transient
## 917 0 No Deposit 250 0 Transient
## 918 0 No Deposit 240 0 Transient
## 919 0 No Deposit 240 0 Transient
## 920 0 No Deposit 240 0 Transient
## 921 0 No Deposit 240 0 Transient
## 922 0 No Deposit 241 0 Transient
## 923 0 No Deposit 240 0 Transient
## 924 0 No Deposit 240 0 Transient
## 925 3 No Deposit 250 0 Transient
## 926 0 No Deposit 142 0 Contract
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## 928 0 No Deposit 240 0 Transient
## 929 0 No Deposit 175 0 Transient
## 930 0 No Deposit 240 0 Transient
## 931 0 No Deposit 240 0 Transient
## 932 0 No Deposit 250 0 Transient
## 933 0 No Deposit 250 0 Transient
## 934 0 No Deposit 250 0 Transient
## 935 0 No Deposit 240 0 Transient
## 936 1 No Deposit 240 0 Transient
## 937 2 No Deposit 250 0 Transient
## 938 1 No Deposit 250 0 Transient
## 939 0 No Deposit 250 0 Transient
## 940 1 No Deposit 250 0 Transient
## 941 0 No Deposit 240 0 Transient
## 942 0 No Deposit 142 0 Contract
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## 944 0 No Deposit 240 0 Transient
## 945 2 No Deposit 240 0 Transient-Party
## 946 2 No Deposit 240 0 Transient-Party
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## 948 0 No Deposit 240 0 Transient
## 949 0 No Deposit 40 0 Contract
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## 951 0 No Deposit 240 0 Transient
## 952 0 No Deposit 241 0 Transient
## 953 0 No Deposit 241 0 Transient
## 954 2 No Deposit 250 0 Transient
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## 980 1 No Deposit 240 0 Transient
## 981 0 No Deposit 250 0 Transient
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## 984 0 No Deposit 14 0 Transient
## 985 0 No Deposit 240 0 Transient
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## 990 1 No Deposit 240 0 Transient
## 991 0 No Deposit 240 0 Transient
## 992 0 No Deposit 240 0 Transient
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## 994 0 No Deposit 142 0 Contract
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## 996 0 No Deposit 240 0 Transient
## 997 0 No Deposit 40 0 Transient
## 998 0 No Deposit 240 0 Transient
## 999 0 No Deposit 14 0 Transient
## 1000 0 No Deposit 14 0 Transient
## 1001 0 No Deposit 250 0 Transient
## 1002 0 No Deposit 240 0 Transient
## 1003 0 No Deposit 240 0 Transient
## 1004 0 No Deposit 250 0 Transient
## 1005 0 No Deposit 40 0 Transient
## 1006 0 No Deposit 142 0 Contract
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## 1008 0 No Deposit 250 0 Transient
## 1009 2 No Deposit 96 0 Transient
## 1010 0 No Deposit 241 0 Transient
## 1011 0 No Deposit 2 0 Contract
## 1012 0 No Deposit 240 0 Transient
## 1013 0 No Deposit 250 0 Transient
## 1014 2 No Deposit 241 0 Transient
## 1015 0 No Deposit 240 0 Transient
## 1016 0 No Deposit 240 0 Transient
## 1017 0 No Deposit 250 0 Transient
## 1018 1 No Deposit 250 0 Transient
## 1019 1 No Deposit 240 0 Transient
## 1020 0 No Deposit 8 0 Contract
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## 1022 1 No Deposit 243 0 Transient
## 1023 0 No Deposit 250 0 Transient
## 1024 0 No Deposit 96 0 Transient
## 1025 1 No Deposit 240 0 Transient
## 1026 0 No Deposit 250 0 Transient
## 1027 0 No Deposit 40 0 Contract
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## 1029 0 No Deposit 171 0 Transient
## 1030 0 No Deposit 142 0 Contract
## 1031 0 No Deposit 240 0 Transient
## 1032 0 No Deposit 240 0 Transient
## 1033 0 No Deposit 175 0 Transient
## 1034 0 No Deposit 240 0 Transient
## 1035 1 No Deposit 250 0 Transient
## 1036 3 No Deposit 14 0 Transient
## 1037 2 No Deposit 240 0 Transient
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## 1039 17 No Deposit 240 0 Transient
## 1040 0 No Deposit 40 0 Transient
## 1041 1 No Deposit 14 0 Transient
## 1042 0 No Deposit 250 0 Transient
## 1043 0 No Deposit 156 0 Contract
## 1044 0 No Deposit 40 0 Contract
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## 1046 0 No Deposit 240 0 Transient
## 1047 0 No Deposit 250 0 Transient
## 1048 0 No Deposit 40 0 Contract
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## 1050 0 No Deposit 14 0 Transient
## 1051 0 No Deposit 251 0 Transient
## 1052 1 No Deposit 240 0 Transient
## 1053 0 No Deposit 240 0 Transient
## 1054 0 No Deposit 14 0 Transient
## 1055 0 No Deposit 241 0 Transient
## 1056 1 No Deposit 240 0 Transient
## 1057 1 No Deposit 240 0 Transient
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## 1060 0 No Deposit 240 0 Transient
## 1061 1 No Deposit 240 0 Transient
## 1062 0 No Deposit 240 0 Transient
## 1063 0 No Deposit 26 0 Contract
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## 1065 1 No Deposit 240 0 Transient
## 1066 0 No Deposit 240 0 Transient
## 1067 0 No Deposit 147 0 Transient
## 1068 0 No Deposit 240 0 Transient
## 1069 0 No Deposit 14 0 Transient
## 1070 1 No Deposit 241 0 Transient
## 1071 0 No Deposit 241 0 Transient
## 1072 1 No Deposit 177 0 Transient
## 1073 0 No Deposit 177 0 Transient
## 1074 0 No Deposit 40 0 Transient
## 1075 0 No Deposit 243 0 Contract
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## 1077 0 No Deposit 40 0 Contract
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## 1079 0 No Deposit 156 0 Contract
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## 1081 0 No Deposit 241 0 Transient
## 1082 0 No Deposit 14 0 Transient
## 1083 0 No Deposit 251 0 Transient
## 1084 0 No Deposit 142 0 Transient
## 1085 0 No Deposit 240 0 Transient
## 1086 0 No Deposit 14 0 Transient
## 1087 0 No Deposit 14 0 Transient
## 1088 0 No Deposit 240 0 Transient
## 1089 0 No Deposit 240 0 Transient
## 1090 0 No Deposit 250 0 Transient
## 1091 0 No Deposit 240 0 Transient
## 1092 0 No Deposit 240 0 Group
## 1093 0 No Deposit 240 0 Transient
## 1094 0 No Deposit 240 0 Transient
## 1095 0 No Deposit 40 0 Contract
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## 1097 0 No Deposit 40 0 Contract
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## 1099 0 No Deposit 240 0 Transient
## 1100 0 No Deposit 275 0 Transient
## 1101 0 No Deposit 240 0 Transient
## 1102 0 No Deposit 241 0 Transient
## 1103 0 No Deposit 250 0 Transient
## 1104 0 No Deposit 69 0 Contract
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## 1106 0 No Deposit 14 0 Transient
## 1107 0 No Deposit 242 0 Transient
## 1108 0 No Deposit 240 0 Transient
## 1109 0 No Deposit 14 0 Transient
## 1110 0 No Deposit 250 0 Transient
## 1111 0 No Deposit 40 0 Contract
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## 1113 0 No Deposit 142 0 Contract
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## 1116 0 No Deposit 40 0 Contract
## 1117 0 No Deposit 243 0 Contract
## 1118 0 No Deposit 240 0 Transient
## 1119 0 No Deposit 240 0 Transient
## 1120 0 No Deposit 96 0 Transient
## 1121 2 No Deposit 241 0 Transient
## 1122 0 No Deposit 142 0 Transient
## 1123 0 No Deposit 240 0 Transient
## 1124 0 No Deposit 240 0 Transient
## 1125 0 No Deposit 250 0 Transient
## 1126 0 No Deposit 240 0 Transient
## 1127 1 No Deposit 241 0 Transient
## 1128 0 No Deposit 2 0 Contract
## 1129 0 No Deposit 240 0 Transient
## 1130 0 No Deposit 250 0 Transient
## 1131 0 No Deposit 147 0 Transient
## 1132 0 No Deposit 240 0 Transient
## 1133 0 No Deposit 40 0 Transient
## 1134 1 No Deposit 40 0 Contract
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## 1136 0 No Deposit 240 0 Transient
## 1137 0 No Deposit 240 0 Transient
## 1138 0 No Deposit 240 0 Transient
## 1139 0 No Deposit 14 0 Transient
## 1140 0 No Deposit 251 0 Transient
## 1141 0 No Deposit 240 0 Transient
## 1142 1 No Deposit 240 0 Transient
## 1143 0 No Deposit 240 0 Transient
## 1144 1 No Deposit 14 0 Transient
## 1145 0 No Deposit 240 0 Transient
## 1146 0 No Deposit 240 0 Transient
## 1147 0 No Deposit 250 0 Transient
## 1148 0 No Deposit 240 0 Transient
## 1149 0 No Deposit 250 0 Transient
## 1150 0 No Deposit 250 0 Transient
## 1151 0 No Deposit 240 0 Transient
## 1152 0 No Deposit 240 0 Transient
## 1153 1 No Deposit 240 0 Transient
## 1154 0 No Deposit 40 0 Contract
## 1155 0 No Deposit 5 0 Transient
## 1156 0 No Deposit 5 0 Transient
## 1157 0 No Deposit 142 0 Contract
## 1158 0 No Deposit 240 0 Transient
## 1159 0 No Deposit 240 0 Transient
## 1160 0 No Deposit 240 0 Transient
## 1161 1 No Deposit 14 0 Transient
## 1162 1 No Deposit 250 0 Transient-Party
## 1163 1 No Deposit 250 0 Transient-Party
## 1164 1 No Deposit 250 0 Transient-Party
## 1165 1 No Deposit 250 0 Transient-Party
## 1166 1 No Deposit 250 0 Transient-Party
## 1167 0 No Deposit 240 0 Transient
## 1168 1 No Deposit 250 0 Transient-Party
## 1169 0 No Deposit 250 0 Transient-Party
## 1170 0 No Deposit 175 0 Transient
## 1171 0 No Deposit 2 0 Contract
## 1172 0 No Deposit 240 0 Transient
## 1173 0 No Deposit 250 0 Transient
## 1174 0 No Deposit 142 0 Contract
## 1175 0 No Deposit 243 0 Contract
## 1176 0 No Deposit 250 0 Transient
## 1177 0 No Deposit 250 0 Transient
## 1178 0 No Deposit 240 0 Transient
## 1179 1 No Deposit 250 0 Transient
## 1180 0 No Deposit 142 0 Transient
## 1181 1 No Deposit 196 0 Transient
## 1182 0 No Deposit 71 0 Contract
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## 1184 0 No Deposit 115 0 Transient
## 1185 0 No Deposit 248 0 Transient-Party
## 1186 0 No Deposit 240 0 Transient
## 1187 0 No Deposit 250 0 Transient
## 1188 0 No Deposit 240 0 Transient
## 1189 0 No Deposit 240 0 Transient
## 1190 0 No Deposit 156 0 Transient
## 1191 0 No Deposit 40 0 Transient
## 1192 0 No Deposit 241 0 Transient
## 1193 1 No Deposit 156 0 Transient
## 1194 0 No Deposit 241 0 Transient
## 1195 0 No Deposit 240 0 Transient
## 1196 0 No Deposit 175 0 Transient-Party
## 1197 1 No Deposit 241 0 Transient
## 1198 0 No Deposit 175 0 Transient-Party
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## 1200 0 No Deposit 134 0 Transient
## 1201 0 No Deposit 40 0 Transient
## 1202 0 No Deposit 241 0 Transient
## 1203 1 No Deposit 243 0 Contract
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## 1207 0 No Deposit 240 0 Transient
## 1208 0 No Deposit 242 0 Transient
## 1209 0 No Deposit 208 0 Transient-Party
## 1210 0 No Deposit 240 0 Transient
## 1211 1 No Deposit 240 0 Transient
## 1212 0 No Deposit 175 0 Transient
## 1213 0 No Deposit 8 0 Transient
## 1214 0 No Deposit 40 0 Contract
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## 1216 0 No Deposit 147 0 Transient
## 1217 0 No Deposit 26 0 Transient
## 1218 0 No Deposit 241 0 Transient
## 1219 0 No Deposit 147 0 Transient
## 1220 0 No Deposit 147 0 Transient
## 1221 0 No Deposit 142 0 Transient
## 1222 0 No Deposit 71 0 Contract
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## 1224 0 No Deposit 240 0 Transient
## 1225 0 No Deposit 14 0 Transient
## 1226 0 No Deposit 240 0 Transient
## 1227 0 No Deposit 5 0 Transient
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## 1233 0 No Deposit 241 0 Transient
## 1234 0 No Deposit 175 0 Transient
## 1235 0 No Deposit 240 0 Transient
## 1236 1 No Deposit 14 0 Transient
## 1237 0 No Deposit 69 0 Contract
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## 1239 0 No Deposit 115 0 Contract
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## 1241 0 No Deposit 240 0 Transient
## 1242 0 No Deposit 175 0 Transient
## 1243 0 No Deposit 240 0 Transient
## 1244 0 No Deposit 240 0 Transient
## 1245 0 No Deposit 40 0 Contract
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## 1247 0 No Deposit 40 0 Contract
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## 1252 4 No Deposit 14 0 Transient-Party
## 1253 0 No Deposit 14 0 Transient
## 1254 0 No Deposit 240 0 Transient
## 1255 1 No Deposit 240 0 Transient
## 1256 0 No Deposit 240 0 Transient
## 1257 1 No Deposit 240 0 Transient
## 1258 0 No Deposit 250 0 Transient
## 1259 0 No Deposit 250 0 Transient
## 1260 0 No Deposit 171 0 Transient
## 1261 0 No Deposit 240 0 Transient
## 1262 0 No Deposit 241 0 Transient
## 1263 0 No Deposit 241 0 Transient
## 1264 0 No Deposit 241 0 Transient
## 1265 0 No Deposit 240 0 Transient
## 1266 0 No Deposit 14 0 Transient-Party
## 1267 0 No Deposit 250 0 Transient
## 1268 1 No Deposit 242 0 Transient
## 1269 0 No Deposit 240 0 Transient
## 1270 0 No Deposit 14 0 Transient
## 1271 0 No Deposit 36 0 Transient
## 1272 0 No Deposit 240 0 Transient
## 1273 0 No Deposit 242 0 Transient
## 1274 0 No Deposit 14 0 Transient-Party
## 1275 0 No Deposit 251 0 Transient
## 1276 0 No Deposit 240 0 Transient
## 1277 0 No Deposit 243 0 Transient
## 1278 0 No Deposit 40 0 Contract
## 1279 0 No Deposit 243 0 Contract
## 1280 0 No Deposit 243 0 Contract
## 1281 0 No Deposit 142 0 Contract
## 1282 0 No Deposit 243 0 Contract
## 1283 0 No Deposit 240 0 Transient
## 1284 1 No Deposit 240 0 Transient
## 1285 0 No Deposit 240 0 Transient
## 1286 0 No Deposit 243 0 Contract
## 1287 0 No Deposit 241 0 Transient
## 1288 0 No Deposit 240 0 Transient
## 1289 0 No Deposit 240 0 Transient
## 1290 0 No Deposit 115 0 Transient
## 1291 0 No Deposit 40 0 Contract
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## 1293 0 No Deposit 69 0 Contract
## 1294 0 No Deposit 240 0 Transient
## 1295 0 No Deposit 6 0 Transient
## 1296 0 No Deposit 171 0 Transient
## 1297 0 No Deposit 8 0 Contract
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## 1299 0 No Deposit 175 0 Transient
## 1300 0 No Deposit 69 0 Contract
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## 1302 0 No Deposit 36 0 Transient
## 1303 0 No Deposit 240 0 Transient
## 1304 1 No Deposit 14 0 Transient
## 1305 0 No Deposit 14 0 Transient
## 1306 0 No Deposit 110 0 Transient
## 1307 1 No Deposit 14 0 Transient
## 1308 0 No Deposit 256 0 Transient-Party
## 1309 0 No Deposit 256 0 Transient-Party
## 1310 0 No Deposit 256 0 Transient-Party
## 1311 0 No Deposit 240 0 Transient
## 1312 0 No Deposit 256 0 Transient-Party
## 1313 0 No Deposit 256 0 Transient-Party
## 1314 0 No Deposit 256 0 Transient-Party
## 1315 0 No Deposit 256 0 Transient-Party
## 1316 0 No Deposit 240 0 Transient
## 1317 0 No Deposit 240 0 Transient
## 1318 0 No Deposit 171 0 Transient
## 1319 0 No Deposit 2 0 Transient
## 1320 0 No Deposit 171 0 Transient
## 1321 0 No Deposit 171 0 Transient
## 1322 0 No Deposit 241 0 Transient
## 1323 0 No Deposit 171 0 Transient
## 1324 0 No Deposit 241 0 Transient
## 1325 0 No Deposit 142 0 Contract
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## 1327 0 No Deposit 14 0 Transient
## 1328 0 No Deposit 240 0 Transient
## 1329 0 No Deposit 240 0 Transient
## 1330 0 No Deposit 240 0 Transient
## 1331 0 No Deposit 250 0 Transient
## 1332 0 No Deposit 240 0 Transient
## 1333 0 No Deposit 250 0 Transient
## 1334 0 No Deposit 115 0 Contract
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## 1336 0 No Deposit 14 0 Transient
## 1337 0 No Deposit 314 0 Contract
## 1338 0 No Deposit 250 0 Transient
## 1339 0 No Deposit 2 0 Contract
## 1340 0 No Deposit 243 0 Contract
## 1341 0 No Deposit 243 0 Contract
## 1342 0 No Deposit 240 0 Transient
## 1343 0 No Deposit 147 0 Transient
## 1344 0 No Deposit 243 0 Transient
## 1345 0 No Deposit 240 0 Transient
## 1346 0 No Deposit 243 0 Contract
## 1347 0 No Deposit 240 0 Transient
## 1348 0 No Deposit 240 0 Transient
## 1349 0 No Deposit 240 0 Transient
## 1350 0 No Deposit 14 0 Transient
## 1351 0 No Deposit 240 0 Transient
## 1352 1 No Deposit 240 0 Transient
## 1353 0 No Deposit 240 0 Transient
## 1354 0 No Deposit 171 0 Transient-Party
## 1355 0 No Deposit 71 0 Contract
## 1356 0 No Deposit 240 0 Transient
## 1357 0 No Deposit 171 0 Transient
## 1358 0 No Deposit 240 0 Transient
## 1359 1 No Deposit 71 0 Transient
## 1360 0 No Deposit 40 0 Contract
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## 1362 0 No Deposit 240 0 Transient
## 1363 0 No Deposit 240 0 Transient
## 1364 0 No Deposit 240 0 Transient
## 1365 0 No Deposit 14 0 Transient
## 1366 0 No Deposit 240 0 Transient-Party
## 1367 0 No Deposit 250 0 Transient
## 1368 0 No Deposit 14 0 Transient
## 1369 0 No Deposit 14 0 Transient
## 1370 0 No Deposit 240 0 Transient-Party
## 1371 0 No Deposit 240 0 Transient-Party
## 1372 0 No Deposit 240 0 Transient-Party
## 1373 0 No Deposit 314 0 Contract
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## 1375 0 No Deposit 240 0 Transient-Party
## 1376 0 No Deposit 240 0 Transient
## 1377 0 No Deposit 240 0 Transient
## 1378 0 No Deposit 240 0 Transient
## 1379 0 No Deposit 240 0 Transient
## 1380 0 No Deposit 241 0 Transient
## 1381 0 No Deposit 36 0 Transient
## 1382 0 No Deposit 250 0 Transient
## 1383 0 No Deposit 40 0 Contract
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## 1385 3 No Deposit 240 0 Transient
## 1386 0 No Deposit 240 0 Transient
## 1387 0 No Deposit 240 0 Transient
## 1388 0 No Deposit 250 0 Transient
## 1389 1 No Deposit 14 0 Transient
## 1390 0 No Deposit 110 0 Transient
## 1391 1 No Deposit 240 0 Transient
## 1392 0 No Deposit 240 0 Transient-Party
## 1393 0 No Deposit 240 0 Transient-Party
## 1394 0 No Deposit 175 0 Transient
## 1395 0 No Deposit 250 0 Transient
## 1396 1 No Deposit 240 0 Transient
## 1397 0 No Deposit 40 0 Contract
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## 1399 0 No Deposit 6 0 Transient
## 1400 0 No Deposit 240 0 Transient
## 1401 0 No Deposit 240 0 Transient
## 1402 0 No Deposit 240 0 Transient
## 1403 0 No Deposit 240 0 Transient
## 1404 0 No Deposit 240 0 Group
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## 1406 0 No Deposit 14 0 Transient
## 1407 0 No Deposit 2 0 Contract
## 1408 0 No Deposit 14 0 Transient
## 1409 0 No Deposit 171 0 Transient
## 1410 1 No Deposit 241 0 Transient
## 1411 0 No Deposit 240 0 Transient
## 1412 0 No Deposit 156 0 Transient
## 1413 0 No Deposit 14 0 Transient
## 1414 0 No Deposit 241 0 Transient
## 1415 1 No Deposit 14 0 Transient
## 1416 0 No Deposit 240 0 Transient
## 1417 0 No Deposit 240 0 Transient
## 1418 0 No Deposit 250 0 Transient
## 1419 0 No Deposit 240 0 Transient
## 1420 0 No Deposit 240 0 Transient
## 1421 0 No Deposit 147 0 Transient
## 1422 0 No Deposit 250 0 Transient
## 1423 0 No Deposit 240 0 Transient
## 1424 0 No Deposit 14 0 Transient
## 1425 1 No Deposit 14 0 Transient
## 1426 1 No Deposit 14 0 Transient
## 1427 0 No Deposit 250 0 Transient
## 1428 0 No Deposit 241 0 Transient
## 1429 0 No Deposit 36 0 Transient
## 1430 1 No Deposit 14 0 Transient
## 1431 0 No Deposit 6 0 Transient
## 1432 0 No Deposit 177 0 Transient-Party
## 1433 0 No Deposit 314 0 Transient
## 1434 0 No Deposit 177 0 Transient-Party
## 1435 0 No Deposit 171 0 Transient-Party
## 1436 0 No Deposit 171 0 Transient-Party
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## 1438 0 No Deposit 240 0 Transient
## 1439 0 No Deposit 240 0 Transient
## 1440 0 No Deposit 250 0 Transient
## 1441 3 No Deposit 14 0 Transient-Party
## 1442 0 No Deposit 250 0 Transient
## 1443 0 No Deposit 14 0 Contract
## 1444 1 No Deposit 250 0 Contract
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## 1446 1 No Deposit 14 0 Contract
## 1447 0 No Deposit 250 0 Contract
## 1448 0 No Deposit 250 0 Contract
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## 1450 2 No Deposit 14 0 Transient-Party
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## 1452 1 No Deposit 250 0 Contract
## 1453 0 No Deposit 281 50 Contract
## 1454 0 No Deposit 250 0 Contract
## 1455 0 No Deposit 281 0 Contract
## 1456 0 No Deposit 250 0 Contract
## 1457 0 No Deposit 240 0 Transient
## 1458 0 No Deposit 314 0 Transient-Party
## 1459 0 No Deposit 241 0 Transient
## 1460 0 No Deposit 8 0 Contract
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## 1462 0 No Deposit 250 0 Transient
## 1463 2 No Deposit 314 0 Transient
## 1464 0 No Deposit 240 0 Transient
## 1465 0 No Deposit 14 0 Transient-Party
## 1466 0 No Deposit 250 0 Transient
## 1467 0 No Deposit 250 0 Contract
## 1468 0 No Deposit 250 0 Contract
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## 1470 0 No Deposit 240 0 Transient
## 1471 0 No Deposit 250 0 Contract
## 1472 0 No Deposit 241 0 Transient
## 1473 0 No Deposit 240 0 Transient
## 1474 1 No Deposit 240 0 Transient
## 1475 0 No Deposit 250 0 Contract
## 1476 0 No Deposit 250 0 Contract
## 1477 0 No Deposit 240 0 Transient
## 1478 1 No Deposit 314 0 Transient
## 1479 0 No Deposit 241 0 Transient
## 1480 0 No Deposit 250 0 Transient
## 1481 0 No Deposit 240 0 Transient
## 1482 0 No Deposit 241 0 Transient
## 1483 0 No Deposit 314 0 Transient
## 1484 0 No Deposit 314 0 Transient
## 1485 0 No Deposit 196 0 Transient
## 1486 0 No Deposit 240 0 Transient
## 1487 0 No Deposit 14 0 Transient
## 1488 0 No Deposit 240 0 Transient
## 1489 0 No Deposit 240 0 Transient
## 1490 0 No Deposit 240 0 Transient
## 1491 0 No Deposit 175 0 Transient
## 1492 0 No Deposit 171 0 Transient
## 1493 0 No Deposit 281 0 Contract
## 1494 0 No Deposit 281 0 Contract
## 1495 0 No Deposit 240 0 Transient
## 1496 0 No Deposit 240 0 Transient
## 1497 0 No Deposit 250 0 Transient
## 1498 1 No Deposit 240 0 Transient
## 1499 0 No Deposit 240 0 Transient
## 1500 1 No Deposit 240 0 Transient
## 1501 0 No Deposit 240 0 Transient
## 1502 0 No Deposit 240 0 Transient
## 1503 0 No Deposit 240 0 Transient
## 1504 0 No Deposit 240 0 Transient
## 1505 0 No Deposit 14 0 Transient
## 1506 0 No Deposit 250 0 Transient
## 1507 1 No Deposit 240 0 Transient
## 1508 0 No Deposit 240 0 Transient
## 1509 0 No Deposit 240 0 Transient
## 1510 0 No Deposit 14 0 Transient
## 1511 0 No Deposit 250 0 Transient
## 1512 1 No Deposit 14 0 Transient
## 1513 0 No Deposit 240 0 Transient
## 1514 0 No Deposit 240 0 Transient
## 1515 0 No Deposit 240 0 Transient
## 1516 2 No Deposit 171 0 Transient
## 1517 0 No Deposit 240 0 Transient
## 1518 0 No Deposit 250 0 Transient
## 1519 1 No Deposit 240 0 Transient
## 1520 0 No Deposit 240 0 Transient
## 1521 0 No Deposit 314 0 Transient
## 1522 0 No Deposit 240 0 Transient
## 1523 0 No Deposit 240 0 Transient
## 1524 0 No Deposit 240 0 Transient
## 1525 0 No Deposit 240 0 Transient
## 1526 0 No Deposit 36 0 Transient
## 1527 0 No Deposit 241 0 Transient
## 1528 0 No Deposit 242 0 Transient
## 1529 0 No Deposit 14 0 Transient-Party
## 1530 0 No Deposit 240 0 Transient
## 1531 0 No Deposit 14 0 Transient
## 1532 0 No Deposit 14 0 Transient
## 1533 0 No Deposit 14 0 Transient
## 1534 0 No Deposit 96 0 Transient
## 1535 1 No Deposit 240 0 Transient
## 1536 0 No Deposit 14 0 Transient
## 1537 0 No Deposit 241 0 Transient
## 1538 0 No Deposit 273 0 Transient-Party
## 1539 1 No Deposit 147 0 Transient
## 1540 0 No Deposit 240 0 Transient
## 1541 0 No Deposit 240 0 Transient
## 1542 0 No Deposit 14 0 Transient
## 1543 2 No Deposit 240 0 Transient
## 1544 0 No Deposit 14 0 Transient
## 1545 0 No Deposit 240 0 Transient
## 1546 0 No Deposit 240 0 Transient
## 1547 0 No Deposit 250 0 Transient
## 1548 0 No Deposit 250 0 Transient
## 1549 1 No Deposit 5 0 Transient-Party
## 1550 0 No Deposit 240 0 Transient-Party
## 1551 0 No Deposit 240 0 Transient
## 1552 1 No Deposit 5 0 Transient-Party
## 1553 0 No Deposit 240 0 Transient
## 1554 0 No Deposit 240 0 Transient-Party
## 1555 0 No Deposit 242 0 Transient
## 1556 3 No Deposit 14 0 Transient-Party
## 1557 1 No Deposit 14 0 Transient-Party
## 1558 0 No Deposit 250 0 Transient
## 1559 0 No Deposit 8 0 Contract
## 1560 1 No Deposit 240 0 Transient-Party
## 1561 1 No Deposit 240 0 Transient-Party
## 1562 2 No Deposit 240 0 Transient-Party
## 1563 1 No Deposit 14 0 Transient
## 1564 2 No Deposit 14 0 Transient-Party
## 1565 0 No Deposit 14 0 Transient
## 1566 1 No Deposit 14 0 Transient-Party
## 1567 0 No Deposit 241 0 Transient
## 1568 0 No Deposit 240 0 Transient
## 1569 0 No Deposit 240 0 Transient
## 1570 0 No Deposit 240 0 Transient
## 1571 0 No Deposit 314 0 Transient
## 1572 1 No Deposit 14 0 Transient
## 1573 0 No Deposit 240 0 Transient
## 1574 0 No Deposit 240 0 Transient
## 1575 1 No Deposit 250 0 Transient
## 1576 0 No Deposit 243 0 Transient
## 1577 0 No Deposit 241 0 Transient
## 1578 1 No Deposit 241 0 Transient
## 1579 0 No Deposit 243 0 Contract
## 1580 0 No Deposit 243 0 Transient
## 1581 0 No Deposit 240 0 Transient
## 1582 0 No Deposit 147 0 Transient
## 1583 0 No Deposit 14 0 Transient
## 1584 0 No Deposit 240 0 Transient
## 1585 0 No Deposit 8 0 Transient-Party
## 1586 0 No Deposit 8 0 Transient-Party
## 1587 0 No Deposit 14 0 Transient
## 1588 0 No Deposit 127 0 Transient
## 1589 1 No Deposit 240 0 Transient
## 1590 0 No Deposit 240 0 Transient
## 1591 0 No Deposit 240 0 Transient
## 1592 0 No Deposit 241 0 Transient
## 1593 0 No Deposit 14 47 Transient-Party
## 1594 0 No Deposit 14 47 Transient-Party
## 1595 0 No Deposit 14 47 Transient-Party
## 1596 0 No Deposit 14 0 Transient-Party
## 1597 0 No Deposit 14 47 Transient-Party
## 1598 0 No Deposit 14 47 Transient-Party
## 1599 0 No Deposit 14 47 Transient-Party
## 1600 0 No Deposit 14 47 Transient-Party
## 1601 0 No Deposit 14 47 Transient-Party
## 1602 0 No Deposit 14 47 Transient-Party
## 1603 0 No Deposit 14 47 Transient-Party
## 1604 0 No Deposit 14 47 Transient-Party
## 1605 0 No Deposit 14 47 Transient-Party
## 1606 0 No Deposit 14 47 Transient-Party
## 1607 0 No Deposit 14 47 Transient-Party
## 1608 0 No Deposit 14 47 Transient-Party
## 1609 0 No Deposit 14 47 Transient-Party
## 1610 0 No Deposit 14 47 Transient-Party
## 1611 0 No Deposit 240 0 Transient
## 1612 0 No Deposit 14 47 Transient-Party
## 1613 0 No Deposit 14 47 Transient-Party
## 1614 0 No Deposit 14 47 Transient-Party
## 1615 0 No Deposit 14 0 Transient
## 1616 0 No Deposit 14 47 Transient-Party
## 1617 0 No Deposit 40 0 Contract
## 1618 0 No Deposit 40 0 Contract
## 1619 0 No Deposit 147 0 Transient
## 1620 0 No Deposit 314 0 Transient
## 1621 0 No Deposit 171 0 Transient
## 1622 1 No Deposit 14 0 Transient
## 1623 0 No Deposit 14 0 Transient
## 1624 0 No Deposit 14 0 Transient
## 1625 1 No Deposit 250 0 Transient
## 1626 0 No Deposit 240 0 Transient
## 1627 0 No Deposit 314 0 Contract
## 1628 0 No Deposit 171 0 Transient
## 1629 0 No Deposit 115 0 Contract
## 1630 0 No Deposit 314 0 Contract
## 1631 0 No Deposit 40 0 Contract
## 1632 1 No Deposit 240 0 Transient
## 1633 0 No Deposit 240 0 Transient
## 1634 0 No Deposit 240 0 Transient
## 1635 2 No Deposit 208 0 Transient-Party
## 1636 0 No Deposit 208 0 Transient-Party
## 1637 0 No Deposit 241 0 Transient
## 1638 0 No Deposit 240 0 Transient
## 1639 0 No Deposit 241 0 Transient
## 1640 0 No Deposit 177 0 Transient
## 1641 0 No Deposit 240 0 Transient
## 1642 0 No Deposit 250 0 Transient
## 1643 1 No Deposit 240 0 Transient
## 1644 0 No Deposit 240 0 Transient
## 1645 1 No Deposit 14 0 Transient
## 1646 1 No Deposit 240 0 Transient
## 1647 0 No Deposit 240 0 Transient
## 1648 0 No Deposit 240 0 Transient-Party
## 1649 0 No Deposit 241 0 Transient
## 1650 0 No Deposit 2 0 Contract
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## 1652 0 No Deposit 177 0 Transient
## 1653 0 No Deposit 243 0 Transient-Party
## 1654 0 No Deposit 243 0 Transient
## 1655 0 No Deposit 243 0 Transient
## 1656 0 No Deposit 243 0 Transient
## 1657 0 No Deposit 240 0 Transient
## 1658 1 No Deposit 240 0 Transient-Party
## 1659 0 No Deposit 240 0 Transient
## 1660 0 No Deposit 240 0 Transient
## 1661 0 No Deposit 240 0 Transient
## 1662 0 No Deposit 240 0 Transient
## 1663 0 No Deposit 240 0 Transient
## 1664 0 No Deposit 240 0 Transient
## 1665 1 No Deposit 240 0 Transient
## 1666 0 No Deposit 243 0 Transient-Party
## 1667 0 No Deposit 243 0 Transient
## 1668 1 No Deposit 26 0 Contract
## 1669 0 No Deposit 240 0 Transient
## 1670 0 No Deposit 240 0 Transient-Party
## 1671 0 No Deposit 240 0 Transient-Party
## 1672 0 No Deposit 240 0 Transient-Party
## 1673 0 No Deposit 240 0 Transient-Party
## 1674 2 No Deposit 14 0 Transient-Party
## 1675 0 No Deposit 240 0 Transient
## 1676 0 No Deposit 240 0 Transient
## 1677 0 No Deposit 14 0 Transient
## 1678 0 No Deposit 240 0 Transient
## 1679 2 No Deposit 14 0 Transient-Party
## 1680 0 No Deposit 14 0 Transient-Party
## 1681 0 No Deposit 253 0 Transient
## 1682 0 No Deposit 14 0 Transient-Party
## 1683 1 No Deposit 14 0 Transient
## 1684 0 No Deposit 240 0 Transient
## 1685 2 No Deposit 14 0 Transient-Party
## 1686 0 No Deposit 14 0 Transient
## 1687 0 No Deposit 241 0 Transient
## 1688 0 No Deposit 240 0 Transient
## 1689 0 No Deposit 241 0 Transient
## 1690 0 No Deposit 2 0 Contract
## 1691 0 No Deposit 2 0 Contract
## 1692 0 No Deposit 241 0 Transient
## 1693 0 No Deposit 250 0 Transient
## 1694 0 No Deposit 240 0 Transient
## 1695 0 No Deposit 2 0 Transient
## 1696 0 No Deposit 5 0 Transient
## 1697 2 No Deposit 240 0 Transient
## 1698 0 No Deposit 240 0 Transient
## 1699 0 No Deposit 250 0 Transient
## 1700 0 No Deposit 240 0 Transient-Party
## 1701 4 No Deposit 14 0 Transient-Party
## 1702 0 No Deposit 240 0 Transient
## 1703 0 No Deposit 14 0 Transient
## 1704 0 No Deposit 240 0 Transient
## 1705 0 No Deposit 240 0 Transient
## 1706 0 No Deposit 177 0 Transient
## 1707 0 No Deposit 314 0 Transient
## 1708 0 No Deposit 175 0 Transient
## 1709 0 No Deposit 240 0 Transient
## 1710 0 No Deposit 143 0 Contract
## 1711 0 No Deposit 175 0 Transient
## 1712 0 No Deposit 240 0 Transient
## 1713 0 No Deposit 250 0 Transient
## 1714 0 No Deposit 240 0 Transient
## 1715 0 No Deposit 8 0 Contract
## 1716 0 No Deposit 314 0 Contract
## 1717 0 No Deposit 243 0 Contract
## 1718 1 No Deposit 240 0 Transient
## 1719 0 No Deposit 242 0 Transient
## 1720 0 No Deposit 242 0 Transient
## 1721 0 No Deposit 240 0 Transient
## 1722 1 No Deposit 240 0 Transient
## 1723 2 No Deposit 185 0 Transient-Party
## 1724 0 No Deposit 240 0 Transient
## 1725 0 No Deposit 14 0 Transient
## 1726 1 No Deposit 14 0 Transient
## 1727 0 No Deposit 240 0 Transient
## 1728 0 No Deposit 14 0 Transient
## 1729 0 No Deposit 240 0 Transient
## 1730 0 No Deposit 196 0 Transient-Party
## 1731 0 No Deposit 240 0 Transient
## 1732 0 No Deposit 240 0 Transient
## 1733 0 No Deposit 196 0 Transient-Party
## 1734 0 No Deposit 240 0 Transient
## 1735 0 No Deposit 240 0 Transient
## 1736 0 No Deposit 240 0 Transient
## 1737 1 No Deposit 240 0 Transient
## 1738 0 No Deposit 156 0 Transient
## 1739 0 No Deposit 14 0 Transient
## 1740 0 No Deposit 14 0 Transient
## 1741 2 No Deposit 240 0 Transient
## 1742 0 No Deposit 14 0 Transient
## 1743 1 No Deposit 14 0 Transient
## 1744 0 No Deposit 14 0 Transient
## 1745 0 No Deposit 14 0 Transient
## 1746 0 No Deposit 14 0 Transient
## 1747 0 No Deposit 14 0 Transient
## 1748 0 No Deposit 14 0 Transient
## 1749 0 No Deposit 14 0 Transient
## 1750 0 No Deposit 240 0 Transient
## 1751 0 No Deposit 240 0 Transient
## 1752 1 No Deposit 240 0 Transient
## 1753 0 No Deposit 240 0 Transient
## 1754 1 No Deposit 240 0 Transient
## 1755 1 No Deposit 14 0 Transient
## 1756 2 No Deposit 240 0 Transient
## 1757 0 No Deposit 14 0 Transient
## 1758 1 No Deposit 14 0 Transient
## 1759 0 No Deposit 14 0 Transient-Party
## 1760 0 No Deposit 14 0 Transient
## 1761 1 No Deposit 240 0 Transient
## 1762 0 No Deposit 250 0 Transient
## 1763 0 No Deposit 14 0 Transient
## 1764 0 No Deposit 14 0 Transient
## 1765 0 No Deposit 14 0 Transient
## 1766 0 No Deposit 240 0 Transient
## 1767 0 No Deposit 14 0 Transient
## 1768 0 No Deposit 14 0 Transient
## 1769 1 No Deposit 240 0 Transient
## 1770 0 No Deposit 14 0 Transient
## 1771 0 No Deposit 240 0 Transient
## 1772 0 No Deposit 14 0 Transient
## 1773 0 No Deposit 14 0 Transient
## 1774 0 No Deposit 240 0 Transient
## 1775 0 No Deposit 177 0 Transient-Party
## 1776 0 No Deposit 177 0 Transient-Party
## 1777 0 No Deposit 14 0 Transient
## 1778 0 No Deposit 241 0 Transient
## 1779 0 No Deposit 96 0 Transient
## 1780 0 No Deposit 14 0 Transient
## 1781 2 No Deposit 14 0 Transient
## 1782 0 No Deposit 241 0 Transient
## 1783 0 No Deposit 241 0 Transient
## 1784 0 No Deposit 240 0 Transient
## 1785 0 No Deposit 240 0 Transient
## 1786 0 No Deposit 14 0 Transient
## 1787 0 No Deposit 14 0 Transient
## 1788 0 No Deposit 14 0 Transient
## 1789 0 No Deposit 14 0 Transient
## 1790 0 No Deposit 330 0 Transient
## 1791 0 No Deposit 14 0 Transient
## 1792 0 No Deposit 14 0 Transient
## 1793 0 No Deposit 240 0 Transient
## 1794 1 No Deposit 240 0 Transient
## 1795 0 No Deposit 250 0 Transient
## 1796 0 No Deposit 14 0 Transient
## 1797 0 No Deposit 14 0 Transient
## 1798 0 No Deposit 14 0 Transient
## 1799 0 No Deposit 330 0 Transient
## 1800 0 No Deposit 14 0 Transient
## 1801 0 No Deposit 330 0 Transient
## 1802 0 No Deposit 241 0 Transient
## 1803 0 No Deposit 14 0 Transient
## 1804 0 No Deposit 175 0 Transient
## 1805 0 No Deposit 14 0 Transient
## 1806 0 No Deposit 14 0 Transient
## 1807 0 No Deposit 14 0 Transient
## 1808 0 No Deposit 240 0 Transient
## 1809 0 No Deposit 14 0 Transient
## 1810 0 No Deposit 14 0 Transient
## 1811 0 No Deposit 14 0 Transient
## 1812 1 No Deposit 250 0 Transient
## 1813 0 No Deposit 240 0 Transient
## 1814 0 No Deposit 14 0 Transient
## 1815 1 No Deposit 14 0 Transient
## 1816 0 No Deposit 250 0 Transient
## 1817 0 No Deposit 14 0 Transient
## 1818 1 No Deposit 240 0 Transient
## 1819 0 No Deposit 240 0 Transient
## 1820 0 No Deposit 36 0 Transient
## 1821 0 No Deposit 156 0 Transient
## 1822 0 No Deposit 240 0 Transient
## 1823 0 No Deposit 196 0 Transient
## 1824 1 No Deposit 240 0 Transient
## 1825 0 No Deposit 156 0 Contract
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## 1827 0 No Deposit 156 0 Transient
## 1828 0 No Deposit 156 0 Transient
## 1829 0 No Deposit 240 0 Transient
## 1830 1 No Deposit 14 0 Transient
## 1831 1 No Deposit 240 0 Transient
## 1832 0 No Deposit 69 0 Transient
## 1833 0 No Deposit 69 0 Transient
## 1834 0 No Deposit 240 0 Transient
## 1835 0 No Deposit 241 0 Transient
## 1836 0 No Deposit 14 0 Transient
## 1837 0 No Deposit 14 0 Transient
## 1838 0 No Deposit 240 0 Transient
## 1839 0 No Deposit 14 0 Transient
## 1840 0 No Deposit 14 0 Transient
## 1841 0 No Deposit 240 0 Transient
## 1842 1 No Deposit 334 0 Transient-Party
## 1843 1 No Deposit 240 0 Transient
## 1844 0 No Deposit 15 0 Transient
## 1845 0 No Deposit 196 0 Transient
## 1846 0 No Deposit 14 0 Transient
## 1847 0 No Deposit 314 0 Transient
## 1848 0 No Deposit 8 0 Transient
## 1849 0 No Deposit 240 0 Transient
## 1850 2 No Deposit 328 0 Transient-Party
## 1851 2 No Deposit 326 0 Transient-Party
## 1852 1 No Deposit 326 0 Transient
## 1853 2 No Deposit 334 0 Transient-Party
## 1854 6 No Deposit 328 0 Transient
## 1855 1 No Deposit 326 0 Transient
## 1856 2 No Deposit 334 0 Transient-Party
## 1857 2 No Deposit 334 0 Transient-Party
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## 1859 0 No Deposit 240 0 Transient
## 1860 0 No Deposit 240 0 Transient
## 1861 1 No Deposit 240 0 Transient
## 1862 3 No Deposit 185 0 Transient-Party
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## 1864 0 No Deposit 14 0 Transient
## 1865 0 No Deposit 14 0 Transient
## 1866 1 No Deposit 240 0 Transient
## 1867 1 No Deposit 240 0 Transient
## 1868 1 No Deposit 185 0 Transient-Party
## 1869 0 No Deposit 314 0 Contract
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## 1871 1 No Deposit 240 0 Transient
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## 1873 0 No Deposit 240 0 Transient
## 1874 0 No Deposit 250 0 Transient
## 1875 0 No Deposit 115 0 Transient
## 1876 0 No Deposit 240 0 Transient
## 1877 0 No Deposit 240 0 Transient
## 1878 1 No Deposit 241 0 Transient
## 1879 0 No Deposit 156 0 Contract
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## 1883 2 No Deposit 185 0 Transient-Party
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## 1888 0 No Deposit 240 0 Transient
## 1889 0 No Deposit 321 0 Transient
## 1890 0 No Deposit 14 0 Transient
## 1891 0 No Deposit 240 0 Transient
## 1892 1 No Deposit 240 0 Transient
## 1893 0 No Deposit 240 0 Transient
## 1894 0 No Deposit 314 0 Transient
## 1895 0 No Deposit 314 0 Transient
## 1896 0 No Deposit 156 0 Transient
## 1897 0 No Deposit 156 0 Transient
## 1898 1 No Deposit 185 0 Transient-Party
## 1899 0 No Deposit 240 0 Transient
## 1900 2 No Deposit 185 0 Transient-Party
## 1901 2 No Deposit 185 0 Transient-Party
## 1902 1 No Deposit 328 0 Transient-Party
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## 1904 1 No Deposit 185 0 Transient-Party
## 1905 1 No Deposit 185 0 Transient-Party
## 1906 2 No Deposit 185 0 Transient-Party
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## 1908 1 No Deposit 240 0 Transient
## 1909 0 No Deposit 14 0 Transient
## 1910 1 No Deposit 240 0 Transient
## 1911 1 No Deposit 334 0 Transient-Party
## 1912 2 No Deposit 185 0 Transient-Party
## 1913 1 No Deposit 185 0 Transient-Party
## 1914 1 No Deposit 334 0 Transient-Party
## 1915 0 No Deposit 240 0 Transient
## 1916 2 No Deposit 185 0 Transient-Party
## 1917 1 No Deposit 185 0 Transient-Party
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## 1920 0 No Deposit 240 0 Transient
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## 1922 1 No Deposit 240 0 Transient
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## 1925 0 No Deposit 14 0 Transient
## 1926 1 No Deposit 241 0 Transient
## 1927 0 No Deposit 314 0 Contract
## 1928 0 No Deposit 314 0 Contract
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## 1930 0 No Deposit 240 0 Transient
## 1931 0 No Deposit 156 0 Transient
## 1932 0 No Deposit 240 0 Transient
## 1933 2 No Deposit 250 0 Transient
## 1934 0 No Deposit 14 0 Transient
## 1935 1 No Deposit 96 0 Transient
## 1936 4 No Deposit 324 0 Transient-Party
## 1937 0 No Deposit 240 0 Transient
## 1938 0 No Deposit 240 0 Transient
## 1939 1 No Deposit 240 0 Transient
## 1940 1 No Deposit 240 0 Transient
## 1941 0 No Deposit 240 0 Transient
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## 1943 2 No Deposit 185 0 Transient-Party
## 1944 0 No Deposit 240 0 Transient
## 1945 4 No Deposit 324 0 Transient-Party
## 1946 1 No Deposit 240 0 Transient
## 1947 0 No Deposit 240 0 Transient
## 1948 0 No Deposit 240 0 Transient
## 1949 0 No Deposit 240 0 Transient
## 1950 0 No Deposit 240 0 Transient
## 1951 3 No Deposit 324 0 Transient
## 1952 0 No Deposit 240 0 Transient-Party
## 1953 3 No Deposit 324 0 Transient-Party
## 1954 1 No Deposit 185 0 Transient-Party
## 1955 2 No Deposit 334 0 Transient-Party
## 1956 0 No Deposit 240 0 Transient
## 1957 2 No Deposit 334 0 Transient-Party
## 1958 1 No Deposit 334 0 Transient-Party
## 1959 3 No Deposit 334 0 Transient-Party
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## 1963 0 No Deposit 240 0 Transient-Party
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## 1965 1 No Deposit 240 0 Transient
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## 1968 2 No Deposit 334 0 Transient-Party
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## 1971 1 No Deposit 240 0 Transient-Party
## 1972 0 No Deposit 240 0 Transient-Party
## 1973 0 No Deposit 185 0 Transient-Party
## 1974 0 No Deposit 240 0 Transient
## 1975 0 No Deposit 14 0 Transient
## 1976 0 No Deposit 240 0 Transient
## 1977 0 No Deposit 14 0 Transient
## 1978 0 No Deposit 185 0 Transient-Party
## 1979 0 No Deposit 241 0 Transient
## 1980 0 No Deposit 240 0 Transient
## 1981 0 No Deposit 240 0 Transient
## 1982 0 No Deposit 6 0 Transient
## 1983 0 No Deposit 242 0 Transient
## 1984 0 No Deposit 242 0 Transient
## 1985 0 No Deposit 6 0 Transient
## 1986 0 No Deposit 321 0 Transient
## 1987 0 No Deposit 240 0 Transient
## 1988 1 No Deposit 115 0 Transient
## 1989 0 No Deposit 240 0 Transient
## 1990 0 No Deposit 240 0 Transient
## 1991 0 No Deposit 313 65 Transient-Party
## 1992 0 No Deposit 313 65 Transient-Party
## 1993 0 No Deposit 313 65 Transient-Party
## 1994 0 No Deposit 313 0 Transient-Party
## 1995 0 No Deposit 313 65 Transient-Party
## 1996 0 No Deposit 313 65 Transient-Party
## 1997 0 No Deposit 313 65 Transient-Party
## 1998 0 No Deposit 313 0 Transient-Party
## 1999 0 No Deposit 313 65 Transient-Party
## 2000 0 No Deposit 313 65 Transient-Party
## 2001 0 No Deposit 313 65 Transient-Party
## 2002 1 No Deposit 313 65 Transient-Party
## 2003 0 No Deposit 313 65 Transient-Party
## 2004 0 No Deposit 313 65 Transient-Party
## 2005 0 No Deposit 313 65 Transient-Party
## 2006 0 No Deposit 313 0 Transient-Party
## 2007 0 No Deposit 313 65 Transient-Party
## 2008 0 No Deposit 313 65 Transient-Party
## 2009 0 No Deposit 313 65 Transient-Party
## 2010 0 No Deposit 313 0 Transient-Party
## 2011 0 No Deposit 313 0 Transient-Party
## 2012 0 No Deposit 313 65 Transient-Party
## 2013 0 No Deposit 313 0 Transient-Party
## 2014 0 No Deposit 313 65 Transient-Party
## 2015 0 No Deposit 313 65 Transient-Party
## 2016 0 No Deposit 241 0 Transient
## 2017 0 No Deposit 241 0 Transient
## 2018 0 No Deposit 171 0 Transient
## 2019 1 No Deposit 240 0 Transient
## 2020 0 No Deposit 240 0 Transient
## 2021 0 No Deposit 250 0 Transient
## 2022 0 No Deposit 250 0 Transient
## 2023 0 No Deposit 38 0 Transient-Party
## 2024 0 No Deposit 38 0 Transient-Party
## 2025 0 No Deposit 38 0 Transient-Party
## 2026 0 No Deposit 38 0 Transient-Party
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## 2028 1 No Deposit 38 0 Transient-Party
## 2029 0 No Deposit 240 0 Transient
## 2030 0 No Deposit 38 0 Transient-Party
## 2031 0 No Deposit 38 0 Transient-Party
## 2032 0 No Deposit 38 0 Transient-Party
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## 2034 0 No Deposit 38 0 Transient-Party
## 2035 0 No Deposit 38 0 Transient-Party
## 2036 0 No Deposit 38 0 Transient-Party
## 2037 0 No Deposit 38 0 Transient-Party
## 2038 0 No Deposit 38 0 Transient-Party
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## 2040 0 No Deposit 14 0 Transient-Party
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## 2043 0 No Deposit 14 0 Transient-Party
## 2044 0 No Deposit 14 0 Transient-Party
## 2045 0 No Deposit 14 0 Transient-Party
## 2046 0 No Deposit 14 0 Transient-Party
## 2047 0 No Deposit 14 0 Transient-Party
## 2048 0 No Deposit 14 0 Transient-Party
## 2049 1 No Deposit 14 0 Transient-Party
## 2050 0 No Deposit 14 0 Transient-Party
## 2051 0 No Deposit 14 0 Transient-Party
## 2052 2 No Deposit 14 0 Transient-Party
## 2053 0 No Deposit 14 0 Transient-Party
## 2054 0 No Deposit 14 0 Transient-Party
## 2055 0 No Deposit 14 0 Transient-Party
## 2056 0 No Deposit 14 0 Transient-Party
## 2057 0 No Deposit 14 0 Transient-Party
## 2058 0 No Deposit 14 0 Transient-Party
## 2059 0 No Deposit 14 0 Transient-Party
## 2060 0 No Deposit 14 0 Transient-Party
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## 2062 3 No Deposit 14 0 Transient-Party
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## 2064 3 No Deposit 240 0 Transient
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## 2066 0 No Deposit 14 0 Transient-Party
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## 2071 0 No Deposit 14 0 Transient-Party
## 2072 0 No Deposit 14 0 Transient-Party
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## 2075 0 No Deposit 14 0 Transient-Party
## 2076 0 No Deposit 14 0 Transient-Party
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## 2079 3 No Deposit 14 0 Transient-Party
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## 2083 0 No Deposit 14 0 Transient-Party
## 2084 0 No Deposit 14 0 Transient-Party
## 2085 2 No Deposit 14 0 Transient-Party
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## 2087 0 No Deposit 240 0 Transient
## 2088 0 No Deposit 240 0 Transient
## 2089 0 No Deposit 240 0 Transient
## 2090 0 No Deposit 314 0 Transient
## 2091 0 No Deposit 242 0 Transient
## 2092 0 No Deposit 6 0 Transient
## 2093 0 No Deposit 240 0 Transient
## 2094 0 No Deposit 240 0 Transient
## 2095 0 No Deposit 240 0 Transient
## 2096 0 No Deposit 240 0 Transient
## 2097 0 No Deposit 240 0 Transient
## 2098 0 No Deposit 240 0 Transient
## 2099 0 No Deposit 240 0 Transient
## 2100 0 No Deposit 240 0 Transient
## 2101 0 No Deposit 314 0 Transient
## 2102 0 No Deposit 240 0 Transient
## 2103 0 No Deposit 240 0 Transient
## 2104 0 No Deposit 8 0 Contract
## 2105 0 No Deposit 15 0 Transient
## 2106 1 No Deposit 14 0 Transient
## 2107 0 No Deposit 240 0 Transient
## 2108 0 No Deposit 177 0 Transient
## 2109 0 No Deposit 8 0 Transient
## 2110 0 No Deposit 250 0 Transient
## 2111 0 No Deposit 240 0 Transient
## 2112 0 No Deposit 240 0 Transient
## 2113 0 No Deposit 240 0 Transient
## 2114 0 No Deposit 240 0 Transient
## 2115 0 No Deposit 240 0 Transient
## 2116 0 No Deposit 240 0 Transient
## 2117 0 No Deposit 240 0 Transient
## 2118 0 No Deposit 240 0 Transient
## 2119 0 No Deposit 177 0 Transient
## 2120 0 No Deposit 240 0 Transient
## 2121 0 No Deposit 240 0 Transient
## 2122 0 No Deposit 115 0 Transient
## 2123 0 No Deposit 250 0 Transient
## 2124 0 No Deposit 171 0 Transient
## 2125 0 No Deposit 5 0 Transient
## 2126 0 No Deposit 14 0 Transient
## 2127 0 No Deposit 240 0 Transient
## 2128 0 No Deposit 6 0 Transient
## 2129 0 No Deposit 240 0 Transient
## 2130 1 No Deposit 314 0 Contract
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## 2133 0 No Deposit 155 0 Transient-Party
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## 2135 0 No Deposit 155 0 Transient-Party
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## 2148 0 No Deposit 155 0 Transient-Party
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## 2150 2 No Deposit 240 0 Transient
## 2151 2 No Deposit 240 0 Transient
## 2152 4 No Deposit 38 0 Transient-Party
## 2153 0 No Deposit 38 0 Transient-Party
## 2154 0 No Deposit 38 0 Transient-Party
## 2155 0 No Deposit 38 0 Transient-Party
## 2156 1 No Deposit 240 0 Transient
## 2157 0 No Deposit 38 0 Transient-Party
## 2158 0 No Deposit 38 0 Transient-Party
## 2159 1 No Deposit 314 0 Contract
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## 2161 2 No Deposit 240 0 Transient
## 2162 0 No Deposit 38 0 Transient-Party
## 2163 0 No Deposit 38 0 Transient-Party
## 2164 0 No Deposit 250 0 Transient
## 2165 0 No Deposit 38 0 Transient-Party
## 2166 0 No Deposit 38 0 Transient-Party
## 2167 0 No Deposit 38 0 Transient-Party
## 2168 1 No Deposit 38 0 Transient-Party
## 2169 0 No Deposit 175 0 Transient
## 2170 0 No Deposit 240 0 Transient
## 2171 0 No Deposit 8 0 Transient
## 2172 0 No Deposit 241 0 Transient
## 2173 0 No Deposit 241 0 Transient
## 2174 2 No Deposit 240 0 Transient
## 2175 0 No Deposit 115 0 Transient-Party
## 2176 0 No Deposit 115 0 Transient-Party
## 2177 0 No Deposit 115 0 Transient-Party
## 2178 0 No Deposit 115 0 Transient
## 2179 0 No Deposit 115 0 Transient-Party
## 2180 0 No Deposit 115 0 Transient-Party
## 2181 0 No Deposit 241 0 Transient
## 2182 0 No Deposit 240 0 Transient
## 2183 0 No Deposit 115 0 Transient
## 2184 0 No Deposit 241 0 Transient
## 2185 0 No Deposit 156 0 Transient
## 2186 0 No Deposit 241 0 Transient
## 2187 1 No Deposit 240 0 Transient
## 2188 1 No Deposit 196 0 Transient
## 2189 0 No Deposit 6 0 Transient
## 2190 0 No Deposit 240 0 Transient
## 2191 0 No Deposit 115 0 Transient
## 2192 0 No Deposit 177 0 Transient
## 2193 0 No Deposit 240 0 Transient
## 2194 1 No Deposit 240 0 Transient
## 2195 1 No Deposit 8 0 Transient
## 2196 1 No Deposit 240 0 Transient
## 2197 1 No Deposit 240 0 Transient
## 2198 0 No Deposit 177 0 Transient
## 2199 0 No Deposit 240 0 Transient
## 2200 0 No Deposit 147 0 Transient
## 2201 0 No Deposit 6 0 Transient
## 2202 0 No Deposit 241 0 Transient
## 2203 0 No Deposit 241 0 Transient
## 2204 0 No Deposit 196 0 Transient
## 2205 0 No Deposit 250 0 Transient
## 2206 1 No Deposit 314 0 Transient-Party
## 2207 0 No Deposit 240 0 Transient
## 2208 0 No Deposit 314 0 Transient
## 2209 0 No Deposit 314 0 Transient-Party
## 2210 0 No Deposit 314 0 Transient-Party
## 2211 0 No Deposit 156 0 Transient
## 2212 0 No Deposit 314 0 Transient-Party
## 2213 0 No Deposit 314 0 Transient
## 2214 0 No Deposit 335 0 Transient-Party
## 2215 1 No Deposit 314 0 Transient-Party
## 2216 1 No Deposit 314 0 Transient-Party
## 2217 1 No Deposit 314 0 Transient-Party
## 2218 1 No Deposit 314 0 Transient-Party
## 2219 1 No Deposit 240 0 Transient
## 2220 1 No Deposit 242 0 Transient
## 2221 1 No Deposit 242 0 Transient
## 2222 1 No Deposit 242 0 Transient
## 2223 0 No Deposit 240 0 Transient
## 2224 1 No Deposit 241 0 Transient
## 2225 0 No Deposit 156 0 Transient
## 2226 1 No Deposit 6 0 Transient
## 2227 0 No Deposit 240 0 Transient
## 2228 0 No Deposit 6 0 Transient
## 2229 0 No Deposit 240 0 Transient
## 2230 0 No Deposit 14 0 Transient
## 2231 0 No Deposit 14 0 Transient
## 2232 0 No Deposit 14 0 Transient
## 2233 0 No Deposit 240 0 Transient
## 2234 1 No Deposit 14 0 Transient-Party
## 2235 0 No Deposit 250 0 Transient-Party
## 2236 1 No Deposit 14 0 Transient-Party
## 2237 0 No Deposit 250 0 Transient-Party
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## 2239 1 No Deposit 171 0 Transient
## 2240 0 No Deposit 240 0 Transient-Party
## 2241 0 No Deposit 240 0 Transient
## 2242 0 No Deposit 171 0 Transient
## 2243 1 No Deposit 240 0 Transient-Party
## 2244 0 No Deposit 156 0 Transient
## 2245 0 No Deposit 240 0 Transient
## 2246 0 No Deposit 240 0 Transient
## 2247 0 No Deposit 240 0 Transient
## 2248 0 No Deposit 6 0 Transient
## 2249 0 No Deposit 6 0 Transient
## 2250 0 No Deposit 6 0 Transient
## 2251 0 No Deposit 196 0 Transient
## 2252 1 No Deposit 240 0 Transient
## 2253 0 No Deposit 240 0 Transient
## 2254 1 No Deposit 156 0 Contract
## 2255 0 No Deposit 240 0 Transient
## 2256 0 No Deposit 241 0 Transient
## 2257 0 No Deposit 241 0 Transient
## 2258 0 No Deposit 14 0 Transient
## 2259 1 No Deposit 36 0 Transient-Party
## 2260 1 No Deposit 36 0 Transient-Party
## 2261 0 No Deposit 240 0 Transient
## 2262 0 No Deposit 115 0 Transient
## 2263 0 No Deposit 240 0 Transient
## 2264 0 No Deposit 240 0 Transient
## 2265 0 No Deposit 241 0 Transient
## 2266 0 No Deposit 14 0 Transient
## 2267 0 No Deposit 308 122 Transient-Party
## 2268 0 No Deposit 308 122 Transient-Party
## 2269 0 No Deposit 308 122 Transient-Party
## 2270 0 No Deposit 308 122 Transient-Party
## 2271 0 No Deposit 240 0 Transient
## 2272 0 No Deposit 308 122 Transient-Party
## 2273 0 No Deposit 308 122 Transient-Party
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## 2285 0 No Deposit 308 122 Transient-Party
## 2286 0 No Deposit 308 122 Transient-Party
## 2287 0 No Deposit 308 122 Transient-Party
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## 2291 1 No Deposit 308 122 Transient-Party
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## 2293 0 No Deposit 308 122 Transient-Party
## 2294 0 No Deposit 308 122 Transient-Party
## 2295 0 No Deposit 308 122 Transient-Party
## 2296 0 No Deposit 308 122 Transient-Party
## 2297 1 No Deposit 308 122 Transient-Party
## 2298 0 No Deposit 308 122 Transient-Party
## 2299 0 No Deposit 240 0 Transient
## 2300 0 No Deposit 5 0 Transient
## 2301 0 No Deposit 308 122 Transient-Party
## 2302 0 No Deposit 308 122 Transient-Party
## 2303 0 No Deposit 308 122 Transient-Party
## 2304 0 No Deposit 308 122 Transient-Party
## 2305 1 No Deposit 308 122 Transient-Party
## 2306 0 No Deposit 308 122 Transient-Party
## 2307 0 No Deposit 308 122 Transient-Party
## 2308 0 No Deposit 308 122 Transient-Party
## 2309 0 No Deposit 308 122 Transient-Party
## 2310 0 No Deposit 308 122 Transient-Party
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## 2312 0 No Deposit 308 122 Transient-Party
## 2313 0 No Deposit 308 122 Transient-Party
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## 2315 0 No Deposit 308 122 Transient-Party
## 2316 0 No Deposit 308 122 Transient
## 2317 0 No Deposit 308 122 Transient-Party
## 2318 5 No Deposit 308 122 Transient-Party
## 2319 1 No Deposit 308 122 Transient-Party
## 2320 1 No Deposit 14 0 Transient
## 2321 3 No Deposit 250 0 Transient
## 2322 0 No Deposit 240 0 Transient
## 2323 1 No Deposit 240 0 Transient
## 2324 1 No Deposit 250 0 Transient
## 2325 0 No Deposit 14 0 Transient
## 2326 1 No Deposit 314 0 Transient
## 2327 0 No Deposit 240 0 Transient
## 2328 0 No Deposit 14 0 Transient
## 2329 0 No Deposit 14 0 Transient
## 2330 0 No Deposit 14 0 Transient
## 2331 0 No Deposit 14 0 Transient
## 2332 0 No Deposit 156 0 Transient-Party
## 2333 0 No Deposit 156 0 Transient-Party
## 2334 0 No Deposit 156 0 Transient-Party
## 2335 0 No Deposit 240 0 Transient-Party
## 2336 0 No Deposit 240 0 Transient-Party
## 2337 0 No Deposit 241 0 Transient
## 2338 0 No Deposit 241 0 Transient
## 2339 0 No Deposit 240 0 Transient
## 2340 0 No Deposit 240 0 Transient
## 2341 0 No Deposit 240 0 Transient
## 2342 2 No Deposit 240 0 Transient
## 2343 0 No Deposit 8 0 Transient
## 2344 0 No Deposit 115 0 Transient
## 2345 0 No Deposit 240 0 Transient
## 2346 0 No Deposit 171 0 Transient
## 2347 0 No Deposit 314 0 Contract
## 2348 0 No Deposit 242 0 Transient
## 2349 0 No Deposit 14 0 Transient
## 2350 0 No Deposit 241 0 Transient
## 2351 0 No Deposit 241 0 Transient
## 2352 0 No Deposit 240 0 Transient
## 2353 0 No Deposit 8 0 Transient
## 2354 0 No Deposit 240 0 Transient
## 2355 0 No Deposit 14 0 Transient
## 2356 0 No Deposit 14 0 Transient
## 2357 0 No Deposit 240 0 Transient
## 2358 0 No Deposit 250 0 Transient
## 2359 0 No Deposit 36 0 Transient
## 2360 0 No Deposit 6 0 Transient-Party
## 2361 0 No Deposit 6 0 Transient-Party
## 2362 1 No Deposit 314 0 Transient
## 2363 0 No Deposit 115 0 Transient
## 2364 0 No Deposit 115 0 Transient
## 2365 0 No Deposit 241 0 Transient
## 2366 0 No Deposit 240 0 Transient
## 2367 0 No Deposit 240 0 Transient
## 2368 0 No Deposit 240 0 Transient
## 2369 1 No Deposit 241 0 Transient
## 2370 0 No Deposit 332 0 Transient-Party
## 2371 0 No Deposit 332 0 Transient-Party
## 2372 0 No Deposit 332 0 Transient-Party
## 2373 0 No Deposit 332 0 Transient-Party
## 2374 0 No Deposit 332 0 Transient-Party
## 2375 0 No Deposit 332 0 Transient
## 2376 0 No Deposit 332 0 Transient-Party
## 2377 0 No Deposit 332 0 Transient-Party
## 2378 0 No Deposit 332 0 Transient-Party
## 2379 0 No Deposit 332 0 Transient-Party
## 2380 0 No Deposit 14 0 Transient
## 2381 0 No Deposit 240 0 Transient
## 2382 0 No Deposit 240 0 Transient
## 2383 0 No Deposit 240 0 Transient
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## 2457 0 No Deposit 14 0 Transient
## 2458 0 No Deposit 242 0 Transient
## 2459 0 No Deposit 242 0 Transient
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## 2465 1 No Deposit 250 0 Transient
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## 2858 1 No Deposit 40 0 Transient
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## 2867 0 No Deposit 240 0 Transient
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## 2912 0 No Deposit 298 0 Transient-Party
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## 2943 1 No Deposit 240 0 Transient
## 2944 1 No Deposit 171 0 Transient
## 2945 0 No Deposit 298 0 Transient-Party
## 2946 0 No Deposit 298 0 Transient-Party
## 2947 0 No Deposit 298 0 Transient-Party
## 2948 1 No Deposit 298 0 Transient-Party
## 2949 0 No Deposit 298 0 Transient-Party
## 2950 0 No Deposit 298 0 Transient-Party
## 2951 0 No Deposit 298 0 Transient-Party
## 2952 1 No Deposit 298 0 Transient-Party
## 2953 0 No Deposit 298 0 Transient-Party
## 2954 0 No Deposit 298 0 Transient-Party
## 2955 0 No Deposit 298 0 Transient-Party
## 2956 0 No Deposit 298 0 Transient-Party
## 2957 0 No Deposit 298 0 Transient-Party
## 2958 0 No Deposit 298 0 Transient-Party
## 2959 0 No Deposit 298 0 Transient-Party
## 2960 0 No Deposit 298 0 Transient-Party
## 2961 0 No Deposit 298 0 Transient-Party
## 2962 1 No Deposit 298 0 Transient-Party
## 2963 0 No Deposit 298 0 Transient-Party
## 2964 0 No Deposit 298 0 Transient-Party
## 2965 1 No Deposit 298 0 Transient-Party
## 2966 1 No Deposit 298 0 Transient-Party
## 2967 1 No Deposit 298 0 Transient-Party
## 2968 0 No Deposit 298 0 Transient
## 2969 0 No Deposit 298 0 Transient-Party
## 2970 0 No Deposit 298 0 Transient-Party
## 2971 1 No Deposit 298 0 Transient-Party
## 2972 0 No Deposit 298 0 Transient-Party
## 2973 0 No Deposit 298 0 Transient-Party
## 2974 0 No Deposit 298 0 Transient-Party
## 2975 0 No Deposit 115 0 Transient
## 2976 0 No Deposit 115 0 Transient
## 2977 1 No Deposit 315 0 Transient-Party
## 2978 0 No Deposit 40 0 Contract
## 2979 0 No Deposit 40 0 Transient
## 2980 0 No Deposit 40 0 Contract
## 2981 0 No Deposit 240 0 Transient-Party
## 2982 0 No Deposit 240 0 Transient-Party
## 2983 0 No Deposit 240 0 Transient-Party
## 2984 0 No Deposit 240 0 Transient
## 2985 0 No Deposit 240 0 Transient-Party
## 2986 0 No Deposit 36 0 Transient
## 2987 0 No Deposit 314 0 Transient
## 2988 0 No Deposit 143 0 Contract
## 2989 0 No Deposit 143 0 Contract
## 2990 0 No Deposit 143 0 Transient
## 2991 0 No Deposit 175 0 Transient
## 2992 0 No Deposit 240 0 Transient
## 2993 0 No Deposit 69 0 Transient
## 2994 0 No Deposit 40 0 Contract
## 2995 0 No Deposit 40 0 Contract
## 2996 0 No Deposit 156 0 Transient
## 2997 0 No Deposit 40 0 Contract
## 2998 1 No Deposit 240 0 Transient
## 2999 0 No Deposit 40 0 Transient
## 3000 0 No Deposit 14 0 Transient
## 3001 0 No Deposit 250 0 Transient
## 3002 0 No Deposit 240 0 Transient
## 3003 1 No Deposit 14 0 Transient-Party
## 3004 1 No Deposit 40 0 Contract
## 3005 0 No Deposit 241 0 Transient
## 3006 0 No Deposit 40 0 Transient
## 3007 0 No Deposit 40 0 Contract
## 3008 2 No Deposit 14 0 Transient-Party
## 3009 0 No Deposit 240 0 Transient
## 3010 2 No Deposit 14 0 Transient-Party
## 3011 0 No Deposit 14 0 Transient
## 3012 0 No Deposit 250 0 Transient
## 3013 0 No Deposit 240 0 Transient
## 3014 1 No Deposit 14 0 Transient-Party
## 3015 2 No Deposit 14 0 Transient-Party
## 3016 2 No Deposit 14 0 Transient-Party
## 3017 1 No Deposit 14 0 Transient-Party
## 3018 1 No Deposit 14 0 Transient-Party
## 3019 1 No Deposit 14 0 Transient-Party
## 3020 1 No Deposit 14 0 Transient-Party
## 3021 1 No Deposit 14 0 Transient-Party
## 3022 1 No Deposit 14 0 Transient-Party
## 3023 1 No Deposit 14 0 Transient-Party
## 3024 1 No Deposit 14 0 Transient-Party
## 3025 1 No Deposit 14 0 Transient-Party
## 3026 1 No Deposit 14 0 Transient-Party
## 3027 3 No Deposit 14 0 Transient-Party
## 3028 2 No Deposit 14 0 Transient-Party
## 3029 1 Refundable 14 0 Transient-Party
## 3030 2 Refundable 14 0 Transient-Party
## 3031 1 No Deposit 14 0 Transient-Party
## 3032 1 No Deposit 14 0 Transient-Party
## 3033 0 No Deposit 147 0 Transient
## 3034 0 No Deposit 240 0 Transient
## 3035 2 No Deposit 14 0 Transient-Party
## 3036 0 No Deposit 175 0 Transient
## 3037 0 No Deposit 175 0 Transient
## 3038 0 No Deposit 240 0 Transient
## 3039 1 No Deposit 14 0 Transient-Party
## 3040 3 No Deposit 14 0 Transient-Party
## 3041 0 No Deposit 14 0 Transient-Party
## 3042 3 No Deposit 14 0 Transient-Party
## 3043 4 No Deposit 14 0 Transient-Party
## 3044 1 No Deposit 14 0 Transient-Party
## 3045 2 No Deposit 14 0 Transient-Party
## 3046 4 No Deposit 14 0 Transient-Party
## 3047 4 No Deposit 14 0 Transient-Party
## 3048 5 No Deposit 14 0 Transient-Party
## 3049 0 No Deposit 240 0 Transient
## 3050 2 No Deposit 14 0 Transient-Party
## 3051 2 No Deposit 14 0 Transient-Party
## 3052 2 No Deposit 14 0 Transient-Party
## 3053 2 No Deposit 14 0 Transient-Party
## 3054 0 No Deposit 14 0 Transient-Party
## 3055 1 No Deposit 14 0 Transient-Party
## 3056 2 No Deposit 14 0 Transient-Party
## 3057 2 No Deposit 14 0 Transient-Party
## 3058 2 No Deposit 14 0 Transient-Party
## 3059 2 No Deposit 14 0 Transient-Party
## 3060 2 No Deposit 14 0 Transient-Party
## 3061 2 No Deposit 14 0 Transient-Party
## 3062 2 No Deposit 14 0 Transient-Party
## 3063 2 No Deposit 14 0 Transient-Party
## 3064 2 No Deposit 14 0 Transient-Party
## 3065 2 No Deposit 14 0 Transient-Party
## 3066 2 No Deposit 14 0 Transient-Party
## 3067 2 No Deposit 14 0 Transient-Party
## 3068 2 No Deposit 14 0 Transient-Party
## 3069 2 No Deposit 14 0 Transient-Party
## 3070 2 No Deposit 14 0 Transient-Party
## 3071 2 No Deposit 14 0 Transient-Party
## 3072 2 No Deposit 14 0 Transient-Party
## 3073 2 No Deposit 14 0 Transient-Party
## 3074 2 No Deposit 14 0 Transient-Party
## 3075 2 No Deposit 14 0 Transient-Party
## 3076 2 No Deposit 14 0 Transient-Party
## 3077 3 No Deposit 14 0 Transient-Party
## 3078 2 No Deposit 14 0 Transient-Party
## 3079 2 No Deposit 14 0 Transient-Party
## 3080 3 No Deposit 14 0 Transient-Party
## 3081 2 Refundable 14 0 Transient-Party
## 3082 2 Non Refund 14 0 Transient-Party
## 3083 2 No Deposit 14 0 Transient-Party
## 3084 2 No Deposit 14 0 Transient-Party
## 3085 2 No Deposit 14 0 Transient-Party
## 3086 2 No Deposit 14 0 Transient-Party
## 3087 0 No Deposit 156 0 Transient-Party
## 3088 1 No Deposit 40 0 Contract
## 3089 2 No Deposit 156 0 Transient-Party
## 3090 0 No Deposit 156 0 Transient-Party
## 3091 3 Non Refund 14 0 Transient-Party
## 3092 0 No Deposit 240 0 Transient
## 3093 0 No Deposit 40 0 Contract
## 3094 0 No Deposit 240 0 Transient
## 3095 1 No Deposit 240 0 Transient
## 3096 0 No Deposit 40 0 Contract
## 3097 1 No Deposit 298 0 Transient-Party
## 3098 0 No Deposit 298 0 Transient-Party
## 3099 0 No Deposit 298 0 Transient-Party
## 3100 0 No Deposit 298 0 Transient-Party
## 3101 1 No Deposit 298 0 Transient-Party
## 3102 1 No Deposit 298 0 Transient-Party
## 3103 1 No Deposit 298 0 Transient-Party
## 3104 1 No Deposit 298 0 Transient-Party
## 3105 0 No Deposit 298 0 Transient
## 3106 0 No Deposit 298 0 Transient-Party
## 3107 1 No Deposit 298 0 Transient-Party
## 3108 0 No Deposit 298 0 Transient-Party
## 3109 1 No Deposit 298 0 Transient-Party
## 3110 1 No Deposit 298 0 Transient-Party
## 3111 0 No Deposit 298 0 Transient-Party
## 3112 0 No Deposit 314 0 Transient
## 3113 0 No Deposit 298 0 Transient-Party
## 3114 0 No Deposit 298 0 Transient-Party
## 3115 0 No Deposit 298 0 Transient-Party
## 3116 0 No Deposit 40 0 Transient
## 3117 3 No Deposit 40 0 Contract
## 3118 0 No Deposit 14 0 Transient-Party
## 3119 0 No Deposit 14 0 Transient-Party
## 3120 5 No Deposit 14 0 Transient-Party
## 3121 0 No Deposit 242 0 Transient
## 3122 0 No Deposit 242 0 Transient
## 3123 0 No Deposit 242 0 Transient
## 3124 0 No Deposit 75 0 Contract
## adr required_car_parking_spaces total_of_special_requests
## 1 0.00 none 0
## 2 0.00 none 0
## 3 75.00 none 0
## 4 75.00 none 0
## 5 98.00 none 1
## 6 98.00 none 1
## 7 107.00 none 0
## 8 103.00 none 1
## 9 145.00 none 0
## 10 97.00 none 3
## 11 154.77 none 1
## 12 94.71 none 0
## 13 97.00 none 3
## 14 97.50 none 0
## 15 88.20 none 0
## 16 107.42 none 0
## 17 153.00 none 1
## 18 97.29 none 1
## 19 84.67 none 1
## 20 84.67 none 1
## 21 99.67 none 1
## 22 94.95 none 1
## 23 63.60 parking 0
## 24 79.50 none 0
## 25 94.00 none 0
## 26 87.30 parking 1
## 27 62.00 none 2
## 28 63.86 none 0
## 29 65.50 none 0
## 30 98.00 none 0
## 31 108.80 none 1
## 32 137.00 none 1
## 33 79.50 none 0
## 34 123.00 none 0
## 35 137.00 none 1
## 36 110.70 none 2
## 37 58.95 none 1
## 38 82.88 none 2
## 39 82.35 none 0
## 40 119.35 none 1
## 41 67.58 none 2
## 42 56.01 none 0
## 43 110.70 none 0
## 44 88.20 parking 0
## 45 147.00 parking 0
## 46 107.00 parking 2
## 47 147.00 none 0
## 48 117.90 none 2
## 49 123.00 none 0
## 50 98.00 none 1
## 51 117.81 none 0
## 52 135.00 parking 2
## 53 133.00 none 1
## 54 58.95 none 0
## 55 153.00 parking 0
## 56 110.50 none 0
## 57 97.00 none 0
## 58 91.50 none 0
## 59 114.50 none 0
## 60 90.90 parking 0
## 61 122.00 none 0
## 62 110.70 parking 0
## 63 85.86 none 0
## 64 58.95 none 0
## 65 55.68 none 0
## 66 111.15 none 0
## 67 134.73 none 0
## 68 92.45 none 1
## 69 73.80 none 0
## 70 98.00 parking 2
## 71 131.00 none 1
## 72 123.00 none 0
## 73 94.71 none 0
## 74 123.00 none 2
## 75 196.54 none 1
## 76 99.30 parking 2
## 77 90.95 none 0
## 78 92.67 none 1
## 79 71.55 parking 0
## 80 96.49 none 2
## 81 85.80 parking 2
## 82 96.30 none 1
## 83 58.95 none 1
## 84 73.80 none 2
## 85 97.00 none 2
## 86 139.00 none 2
## 87 67.24 none 0
## 88 116.50 none 1
## 89 123.00 none 1
## 90 71.55 none 1
## 91 71.55 none 1
## 92 149.00 none 1
## 93 163.00 parking 0
## 94 79.50 none 0
## 95 0.00 none 1
## 96 123.00 none 0
## 97 73.80 parking 1
## 98 114.50 none 0
## 99 73.80 none 2
## 100 73.80 none 1
## 101 88.20 none 1
## 102 85.10 none 0
## 103 89.00 none 0
## 104 80.10 none 0
## 105 98.00 none 0
## 106 101.00 parking 0
## 107 167.00 none 1
## 108 225.00 parking 0
## 109 97.00 parking 0
## 110 87.30 none 2
## 111 85.59 parking 2
## 112 123.00 none 0
## 113 107.00 none 0
## 114 163.00 none 0
## 115 82.00 none 0
## 116 164.20 parking 1
## 117 97.29 none 1
## 118 98.40 none 0
## 119 175.00 parking 1
## 120 63.60 parking 2
## 121 124.45 parking 1
## 122 110.70 none 0
## 123 74.07 none 0
## 124 134.10 none 0
## 125 167.00 none 0
## 126 123.00 none 2
## 127 117.81 parking 2
## 128 171.32 none 1
## 129 210.00 parking 0
## 130 97.00 none 2
## 131 117.81 none 2
## 132 0.00 none 2
## 133 0.00 none 0
## 134 131.00 none 0
## 135 98.00 none 2
## 136 87.30 parking 1
## 137 97.00 none 1
## 138 91.50 none 0
## 139 73.80 none 1
## 140 123.00 none 0
## 141 175.00 parking 0
## 142 117.80 parking 1
## 143 117.63 none 0
## 144 185.00 none 0
## 145 47.25 none 0
## 146 151.00 none 1
## 147 120.60 none 1
## 148 63.86 none 1
## 149 157.10 none 1
## 150 91.37 none 2
## 151 73.00 none 0
## 152 110.70 none 0
## 153 93.60 none 0
## 154 83.50 none 0
## 155 82.00 parking 2
## 156 185.50 none 1
## 157 0.00 none 1
## 158 0.00 none 1
## 159 109.80 none 3
## 160 195.00 parking 0
## 161 109.80 none 3
## 162 193.00 none 1
## 163 97.00 none 2
## 164 150.00 none 1
## 165 98.00 none 0
## 166 79.50 none 0
## 167 126.65 none 1
## 168 107.00 none 1
## 169 131.00 parking 2
## 170 131.00 parking 1
## 171 146.00 none 1
## 172 110.70 none 1
## 173 61.00 none 0
## 174 104.72 parking 1
## 175 77.96 parking 1
## 176 107.00 none 1
## 177 89.68 none 0
## 178 155.00 none 0
## 179 112.00 none 1
## 180 130.10 none 3
## 181 100.10 none 2
## 182 123.00 none 3
## 183 124.00 parking 1
## 184 146.00 parking 0
## 185 82.00 none 2
## 186 98.00 none 1
## 187 75.46 parking 1
## 188 65.50 none 0
## 189 90.95 none 0
## 190 93.60 none 0
## 191 119.25 none 0
## 192 134.73 none 0
## 193 112.00 none 1
## 194 131.00 none 0
## 195 88.20 none 0
## 196 73.80 none 1
## 197 98.00 none 0
## 198 110.50 none 1
## 199 131.00 none 1
## 200 185.00 parking 1
## 201 87.30 none 2
## 202 73.80 none 1
## 203 120.00 none 1
## 204 98.00 none 1
## 205 96.30 none 2
## 206 197.00 none 1
## 207 197.00 none 1
## 208 82.00 none 2
## 209 165.00 parking 3
## 210 149.40 none 0
## 211 106.84 parking 2
## 212 161.00 parking 1
## 213 134.43 none 2
## 214 144.43 parking 2
## 215 152.00 none 0
## 216 125.54 none 3
## 217 133.83 none 2
## 218 159.75 parking 0
## 219 120.60 none 2
## 220 112.20 none 0
## 221 110.60 none 0
## 222 105.90 none 0
## 223 95.27 none 3
## 224 108.83 none 1
## 225 120.60 none 0
## 226 90.67 none 1
## 227 144.40 none 1
## 228 99.50 parking 2
## 229 139.40 none 1
## 230 89.97 none 1
## 231 123.00 none 0
## 232 123.00 none 0
## 233 123.00 none 0
## 234 144.90 none 1
## 235 153.00 none 0
## 236 131.00 none 1
## 237 100.00 parking 0
## 238 65.50 none 0
## 239 123.00 none 1
## 240 123.00 none 0
## 241 131.00 none 0
## 242 104.68 parking 0
## 243 166.00 none 0
## 244 213.75 none 0
## 245 178.00 parking 0
## 246 113.90 none 1
## 247 129.29 parking 0
## 248 153.00 parking 0
## 249 98.00 parking 2
## 250 169.00 parking 2
## 251 132.29 none 0
## 252 123.00 none 1
## 253 117.22 none 1
## 254 149.00 none 1
## 255 107.00 none 0
## 256 117.22 parking 2
## 257 119.25 none 0
## 258 123.00 none 0
## 259 123.00 none 1
## 260 144.43 parking 2
## 261 148.30 parking 2
## 262 125.22 none 1
## 263 123.00 none 0
## 264 130.00 parking 1
## 265 78.84 none 0
## 266 80.10 none 0
## 267 193.00 none 1
## 268 88.20 none 0
## 269 90.71 none 0
## 270 114.50 none 1
## 271 120.20 none 1
## 272 79.50 none 0
## 273 117.90 none 3
## 274 90.95 none 0
## 275 107.00 none 2
## 276 130.05 parking 1
## 277 123.00 none 0
## 278 196.00 none 1
## 279 149.40 parking 0
## 280 175.71 none 0
## 281 119.70 none 2
## 282 100.86 none 0
## 283 82.00 none 3
## 284 73.80 none 1
## 285 117.90 parking 0
## 286 82.00 none 3
## 287 150.00 none 0
## 288 161.00 parking 2
## 289 64.00 none 2
## 290 110.00 parking 0
## 291 88.20 parking 1
## 292 130.00 parking 0
## 293 88.20 none 1
## 294 165.00 parking 1
## 295 114.00 none 0
## 296 120.00 none 2
## 297 120.00 none 0
## 298 107.20 none 0
## 299 120.00 none 2
## 300 163.80 none 1
## 301 153.00 none 0
## 302 98.50 none 2
## 303 119.70 none 2
## 304 184.60 none 1
## 305 181.22 none 2
## 306 100.20 parking 3
## 307 134.25 parking 0
## 308 96.30 none 0
## 309 83.09 none 2
## 310 134.00 none 0
## 311 8.00 none 1
## 312 146.00 parking 1
## 313 111.15 none 1
## 314 136.20 none 2
## 315 101.80 none 0
## 316 127.00 none 1
## 317 161.50 none 2
## 318 230.67 parking 2
## 319 180.00 parking 2
## 320 168.57 none 1
## 321 107.00 none 1
## 322 73.41 none 0
## 323 109.90 none 2
## 324 109.90 none 2
## 325 187.50 parking 3
## 326 134.00 none 0
## 327 106.90 none 0
## 328 216.13 parking 0
## 329 107.00 none 0
## 330 107.00 none 0
## 331 107.00 none 0
## 332 107.00 none 0
## 333 107.00 none 0
## 334 190.33 none 1
## 335 107.00 none 0
## 336 94.00 none 0
## 337 98.75 none 0
## 338 148.23 none 0
## 339 94.00 none 0
## 340 107.00 none 0
## 341 107.00 none 0
## 342 148.23 none 0
## 343 0.00 none 0
## 344 107.00 none 0
## 345 107.00 none 0
## 346 107.00 none 0
## 347 107.00 none 0
## 348 98.75 none 0
## 349 120.60 none 1
## 350 123.20 none 0
## 351 169.00 parking 1
## 352 198.00 parking 0
## 353 135.60 none 1
## 354 103.50 none 1
## 355 184.00 parking 0
## 356 121.33 none 1
## 357 150.20 none 1
## 358 150.20 none 1
## 359 75.44 none 1
## 360 166.00 none 1
## 361 130.00 none 0
## 362 96.30 none 0
## 363 160.00 none 0
## 364 0.00 none 1
## 365 127.25 none 1
## 366 164.00 none 2
## 367 144.50 none 0
## 368 112.00 none 0
## 369 155.50 parking 0
## 370 144.50 none 0
## 371 144.50 none 0
## 372 123.00 none 0
## 373 120.60 none 1
## 374 107.00 none 0
## 375 133.75 parking 0
## 376 103.50 none 0
## 377 84.80 none 1
## 378 107.00 none 0
## 379 107.00 parking 0
## 380 84.80 none 1
## 381 107.00 parking 0
## 382 195.00 none 1
## 383 164.00 none 0
## 384 195.00 none 0
## 385 125.00 none 2
## 386 132.30 none 3
## 387 145.00 none 2
## 388 92.00 none 0
## 389 132.30 none 3
## 390 147.00 none 2
## 391 167.00 parking 2
## 392 210.00 none 1
## 393 194.90 none 2
## 394 103.50 parking 3
## 395 118.50 none 3
## 396 192.00 none 1
## 397 128.27 none 0
## 398 135.00 none 0
## 399 126.30 none 2
## 400 96.30 none 0
## 401 126.30 none 2
## 402 118.50 none 3
## 403 116.33 parking 1
## 404 164.00 none 1
## 405 147.67 none 2
## 406 145.00 none 0
## 407 147.00 parking 2
## 408 167.00 none 0
## 409 137.75 parking 1
## 410 145.00 none 0
## 411 132.60 parking 0
## 412 132.50 none 0
## 413 98.02 none 1
## 414 126.00 none 2
## 415 173.00 none 1
## 416 249.00 none 0
## 417 186.50 none 0
## 418 95.00 none 1
## 419 241.50 none 2
## 420 177.00 parking 0
## 421 153.73 parking 0
## 422 115.00 none 1
## 423 128.25 none 1
## 424 128.25 none 1
## 425 115.00 none 1
## 426 135.00 none 1
## 427 135.00 none 1
## 428 124.50 none 0
## 429 98.00 none 0
## 430 98.00 parking 0
## 431 135.00 none 0
## 432 88.20 none 0
## 433 120.60 parking 1
## 434 154.50 parking 2
## 435 92.00 none 0
## 436 115.00 none 1
## 437 131.37 none 1
## 438 123.00 none 1
## 439 117.84 none 2
## 440 116.85 none 0
## 441 129.16 none 1
## 442 123.00 none 1
## 443 123.00 none 0
## 444 135.20 none 0
## 445 123.00 none 2
## 446 130.50 none 1
## 447 123.00 none 0
## 448 0.00 none 0
## 449 0.00 none 1
## 450 115.00 none 3
## 451 115.00 none 0
## 452 103.50 parking 2
## 453 157.00 parking 0
## 454 111.65 none 0
## 455 104.00 none 0
## 456 133.17 none 0
## 457 121.01 none 1
## 458 167.50 none 0
## 459 152.43 none 3
## 460 148.34 none 0
## 461 167.00 none 1
## 462 192.50 none 1
## 463 141.60 none 1
## 464 141.60 none 1
## 465 141.60 none 1
## 466 99.50 none 0
## 467 103.50 none 0
## 468 207.00 parking 0
## 469 163.33 parking 1
## 470 151.86 none 0
## 471 95.57 none 1
## 472 112.05 none 1
## 473 160.00 none 1
## 474 103.18 none 1
## 475 153.00 parking 0
## 476 167.69 none 1
## 477 0.00 none 0
## 478 0.00 none 1
## 479 154.00 parking 1
## 480 134.00 none 0
## 481 194.00 parking 1
## 482 184.00 none 1
## 483 101.46 parking 0
## 484 217.05 parking 1
## 485 164.00 none 2
## 486 173.00 none 2
## 487 153.00 none 2
## 488 173.00 none 2
## 489 153.00 none 2
## 490 134.00 none 0
## 491 153.00 none 2
## 492 154.00 none 0
## 493 173.00 none 2
## 494 222.67 none 0
## 495 107.00 none 0
## 496 149.70 parking 1
## 497 107.00 none 0
## 498 75.44 none 1
## 499 75.44 none 1
## 500 115.00 none 0
## 501 154.00 none 2
## 502 166.00 none 0
## 503 132.50 none 0
## 504 0.00 none 0
## 505 133.00 none 0
## 506 107.00 none 0
## 507 117.81 none 0
## 508 107.00 none 0
## 509 147.00 none 0
## 510 153.00 none 1
## 511 118.00 none 0
## 512 134.00 none 1
## 513 135.00 none 2
## 514 158.77 none 2
## 515 134.00 parking 1
## 516 142.03 none 1
## 517 240.00 none 0
## 518 134.00 none 0
## 519 147.00 none 0
## 520 176.00 parking 0
## 521 147.00 none 0
## 522 167.00 none 1
## 523 107.20 parking 0
## 524 233.05 parking 0
## 525 90.12 none 1
## 526 120.60 none 2
## 527 114.25 none 1
## 528 88.55 none 0
## 529 195.00 parking 0
## 530 103.50 none 0
## 531 115.00 none 3
## 532 209.00 none 1
## 533 180.00 none 0
## 534 103.18 none 0
## 535 174.01 none 1
## 536 107.20 none 0
## 537 0.00 none 0
## 538 145.00 parking 0
## 539 145.00 parking 0
## 540 166.00 none 2
## 541 115.00 none 0
## 542 166.00 parking 2
## 543 162.00 none 2
## 544 153.00 none 0
## 545 133.20 none 0
## 546 134.00 none 1
## 547 107.00 parking 2
## 548 145.00 none 2
## 549 145.00 none 0
## 550 167.20 none 0
## 551 130.00 none 0
## 552 149.00 none 0
## 553 154.00 parking 0
## 554 195.00 none 1
## 555 107.20 none 0
## 556 159.60 parking 1
## 557 120.60 none 0
## 558 107.20 none 0
## 559 102.72 none 1
## 560 114.85 none 1
## 561 114.85 none 1
## 562 135.00 parking 1
## 563 107.00 none 1
## 564 107.00 none 1
## 565 164.00 none 2
## 566 138.80 none 1
## 567 135.00 none 0
## 568 103.18 none 1
## 569 90.95 none 1
## 570 0.00 none 0
## 571 146.00 parking 2
## 572 134.00 none 0
## 573 157.00 none 1
## 574 188.00 none 0
## 575 145.00 none 1
## 576 134.00 none 2
## 577 90.95 none 1
## 578 108.06 none 1
## 579 107.00 none 0
## 580 153.00 none 1
## 581 250.33 none 2
## 582 280.74 parking 0
## 583 193.00 none 1
## 584 160.00 parking 3
## 585 125.89 none 1
## 586 125.89 none 1
## 587 0.00 none 0
## 588 0.00 parking 0
## 589 188.00 none 0
## 590 120.00 none 1
## 591 192.00 none 2
## 592 192.00 parking 2
## 593 219.50 none 0
## 594 192.50 none 1
## 595 57.60 none 0
## 596 214.00 parking 0
## 597 157.80 none 0
## 598 170.33 parking 1
## 599 119.25 none 0
## 600 96.30 none 0
## 601 211.00 none 2
## 602 188.29 none 2
## 603 185.00 parking 4
## 604 193.13 none 0
## 605 172.00 none 0
## 606 195.50 parking 1
## 607 186.00 parking 4
## 608 163.50 none 1
## 609 180.28 none 0
## 610 174.76 parking 1
## 611 116.85 none 0
## 612 159.00 none 0
## 613 0.00 none 0
## 614 144.76 none 0
## 615 162.14 none 1
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## 3090 87.46 none 3
## 3091 60.00 none 0
## 3092 59.28 none 2
## 3093 62.48 none 1
## 3094 57.28 none 2
## 3095 57.28 none 2
## 3096 62.48 none 1
## 3097 42.50 none 0
## 3098 85.00 none 0
## 3099 85.00 none 0
## 3100 85.00 none 0
## 3101 54.50 none 0
## 3102 54.50 none 0
## 3103 54.50 none 0
## 3104 54.50 none 0
## 3105 85.00 none 0
## 3106 85.00 none 0
## 3107 54.50 none 0
## 3108 85.00 none 0
## 3109 54.50 none 0
## 3110 54.50 none 0
## 3111 87.00 none 0
## 3112 46.93 none 1
## 3113 85.00 none 0
## 3114 85.00 none 0
## 3115 85.00 none 0
## 3116 82.83 none 0
## 3117 52.58 none 0
## 3118 0.00 none 0
## 3119 0.00 none 0
## 3120 0.00 none 0
## 3121 66.34 none 1
## 3122 68.06 none 1
## 3123 66.34 none 1
## 3124 58.95 none 1
## reservation_status reservation_status_date arrival_date month_year
## 1 Check-Out 2015-07-01 2015-07-01 2015-07
## 2 Check-Out 2015-07-01 2015-07-01 2015-07
## 3 Check-Out 2015-07-02 2015-07-01 2015-07
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## 123 Check-Out 2015-07-19 2015-07-05 2015-07
## 124 Check-Out 2015-07-19 2015-07-05 2015-07
## 125 Check-Out 2015-07-11 2015-07-06 2015-07
## 126 Check-Out 2015-07-10 2015-07-06 2015-07
## 127 Check-Out 2015-07-10 2015-07-06 2015-07
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## 135 Check-Out 2015-07-08 2015-07-06 2015-07
## 136 Check-Out 2015-07-08 2015-07-06 2015-07
## 137 Check-Out 2015-07-09 2015-07-06 2015-07
## 138 Check-Out 2015-07-10 2015-07-06 2015-07
## 139 Check-Out 2015-07-10 2015-07-06 2015-07
## 140 Check-Out 2015-07-11 2015-07-06 2015-07
## 141 Check-Out 2015-07-11 2015-07-06 2015-07
## 142 Check-Out 2015-07-12 2015-07-06 2015-07
## 143 Check-Out 2015-07-12 2015-07-06 2015-07
## 144 Check-Out 2015-07-13 2015-07-06 2015-07
## 145 Check-Out 2015-07-13 2015-07-06 2015-07
## 146 Check-Out 2015-07-14 2015-07-06 2015-07
## 147 Check-Out 2015-07-16 2015-07-06 2015-07
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## 149 Check-Out 2015-07-16 2015-07-06 2015-07
## 150 Check-Out 2015-07-16 2015-07-06 2015-07
## 151 Check-Out 2015-07-17 2015-07-06 2015-07
## 152 Check-Out 2015-07-20 2015-07-06 2015-07
## 153 Check-Out 2015-07-20 2015-07-06 2015-07
## 154 Check-Out 2015-07-13 2015-07-06 2015-07
## 155 Check-Out 2015-07-10 2015-07-07 2015-07
## 156 Check-Out 2015-07-10 2015-07-07 2015-07
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## 165 Check-Out 2015-07-11 2015-07-07 2015-07
## 166 Check-Out 2015-07-14 2015-07-07 2015-07
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## 168 Check-Out 2015-07-11 2015-07-08 2015-07
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## 179 Check-Out 2015-07-16 2015-07-08 2015-07
## 180 Check-Out 2015-07-17 2015-07-08 2015-07
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## 184 Check-Out 2015-07-11 2015-07-09 2015-07
## 185 Check-Out 2015-07-12 2015-07-09 2015-07
## 186 Check-Out 2015-07-13 2015-07-09 2015-07
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## 201 Check-Out 2015-07-12 2015-07-10 2015-07
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## 203 Check-Out 2015-07-13 2015-07-10 2015-07
## 204 Check-Out 2015-07-13 2015-07-10 2015-07
## 205 Check-Out 2015-07-13 2015-07-10 2015-07
## 206 Check-Out 2015-07-14 2015-07-10 2015-07
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## 3123 Check-Out 2016-06-06 2016-05-30 2016-05
## 3124 Check-Out 2016-06-13 2016-05-30 2016-05
## [ reached 'max' / getOption("max.print") -- omitted 71887 rows ]hotel_stays %>%
count(hotel, required_car_parking_spaces, children) %>%
group_by(hotel, children) %>%
mutate(proportion = n / sum(n)) %>%
ggplot(aes(required_car_parking_spaces, proportion, fill = children)) +
geom_col(position = "dodge") +
scale_y_continuous(labels = scales::percent_format()) +
facet_wrap(~hotel, nrow = 2) +
labs(
x = NULL,
y = "Proportion of hotel stays",
fill = NULL
)
Q2. WHich customer type pay more in the hotels (this is for not cancelled one)?
library(GGally)## Warning: package 'GGally' was built under R version 4.3.2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2hotel_stays %>%
select(
hotel, adr,
customer_type,
total_of_special_requests
) %>%
ggpairs(mapping = aes(color = customer_type))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# 3.Which country the guest come from (for non cancelled)? Need to do some work not able to draw the map by plotly
country <- hotel_data %>%
filter(is_canceled==0)%>%
count(country)
country## country n
## 1 ABW 2
## 2 AGO 157
## 3 AIA 1
## 4 ALB 10
## 5 AND 2
## 6 ARE 8
## 7 ARG 160
## 8 ARM 6
## 9 ASM 1
## 10 ATA 2
## 11 ATF 1
## 12 AUS 319
## 13 AUT 1033
## 14 AZE 8
## 15 BDI 1
## 16 BEL 1868
## 17 BFA 1
## 18 BGD 3
## 19 BGR 63
## 20 BHR 1
## 21 BHS 1
## 22 BIH 10
## 23 BLR 17
## 24 BOL 10
## 25 BRA 1392
## 26 BRB 4
## 27 BWA 1
## 28 CAF 5
## 29 CHE 1298
## 30 CHL 49
## 31 CHN 537
## 32 CIV 4
## 33 CMR 10
## 34 CN 1025
## 35 COL 48
## 36 COM 2
## 37 CPV 12
## 38 CRI 18
## 39 CUB 8
## 40 CYM 1
## 41 CYP 40
## 42 CZE 134
## 43 DEU 6067
## 44 DJI 1
## 45 DMA 1
## 46 DNK 326
## 47 DOM 6
## 48 DZA 82
## 49 ECU 19
## 50 EGY 21
## 51 ESP 6383
## 52 EST 65
## 53 ETH 2
## 54 FIN 377
## 55 FRA 8468
## 56 FRO 1
## 57 GAB 2
## 58 GBR 9668
## 59 GEO 7
## 60 GHA 2
## 61 GIB 7
## 62 GNB 8
## 63 GRC 93
## 64 GTM 4
## 65 GUY 1
## 66 HKG 3
## 67 HRV 75
## 68 HUN 153
## 69 IDN 11
## 70 IND 116
## 71 IRL 2542
## 72 IRN 59
## 73 IRQ 14
## 74 ISL 53
## 75 ISR 500
## 76 ITA 2428
## 77 JAM 6
## 78 JOR 18
## 79 JPN 169
## 80 KAZ 14
## 81 KEN 4
## 82 KIR 1
## 83 KNA 2
## 84 KOR 78
## 85 KWT 10
## 86 LAO 2
## 87 LBN 22
## 88 LBY 8
## 89 LCA 1
## 90 LIE 2
## 91 LKA 7
## 92 LTU 74
## 93 LUX 177
## 94 LVA 46
## 95 MAC 1
## 96 MAR 150
## 97 MCO 3
## 98 MDG 1
## 99 MDV 3
## 100 MEX 75
## 101 MKD 8
## 102 MLI 1
## 103 MLT 13
## 104 MMR 1
## 105 MNE 3
## 106 MOZ 48
## 107 MRT 1
## 108 MUS 6
## 109 MWI 2
## 110 MYS 25
## 111 NAM 1
## 112 NCL 1
## 113 NGA 13
## 114 NLD 1716
## 115 NOR 426
## 116 NPL 1
## 117 NULL 421
## 118 NZL 68
## 119 OMN 14
## 120 PAK 5
## 121 PAN 9
## 122 PER 23
## 123 PHL 15
## 124 PLW 1
## 125 POL 703
## 126 PRI 10
## 127 PRT 20977
## 128 PRY 4
## 129 PYF 1
## 130 QAT 4
## 131 ROU 366
## 132 RUS 391
## 133 RWA 2
## 134 SAU 15
## 135 SDN 1
## 136 SEN 3
## 137 SGP 22
## 138 SLE 1
## 139 SLV 2
## 140 SMR 1
## 141 SRB 98
## 142 STP 2
## 143 SUR 5
## 144 SVK 41
## 145 SVN 41
## 146 SWE 793
## 147 SYC 1
## 148 SYR 3
## 149 TGO 2
## 150 THA 41
## 151 TJK 1
## 152 TMP 2
## 153 TUN 20
## 154 TUR 146
## 155 TWN 37
## 156 TZA 2
## 157 UGA 2
## 158 UKR 48
## 159 URY 23
## 160 USA 1592
## 161 UZB 2
## 162 VEN 14
## 163 VNM 6
## 164 ZAF 49
## 165 ZMB 1
## 166 ZWE 2MODEL SUMMARY
Based on the results from the performance metrics calculation of your logistic regression model, here are the interpretations:
Accuracy (0.6961): This metric indicates that approximately 69.61% of the predictions made by your model are correct. It’s a measure of how well the model performs across both classes (canceled and not canceled).
Precision (0.6549): Precision is about 65.49%, showing the proportion of positive identifications (cancellations in this case) that were actually correct. A higher precision rate means fewer false positives.
Recall (0.3921): Recall, or sensitivity, is at 39.21%. This measures how many of the actual positive cases (cancellations) my model correctly predicted. In this context, it indicates that the model captures about 39.21% of all the cancellations.
F1 Score (0.4905): The F1 Score is a balance between precision and recall, calculated as their harmonic mean. An F1 Score of 0.4905 suggests a moderate balance between precision and recall, indicating that the model is neither heavily biased towards precision nor recall.
Interpretation and Implications
Moderate Accuracy: While the model correctly predicts cancellations and non-cancellations with reasonable accuracy, there is still room for improvement. Factors contributing to incorrect predictions should be examined.
Higher Precision than Recall: The model is more reliable in its positive predictions (cancellations) than in identifying all actual cancellations. This might be preferable in scenarios where false positives (predicting a cancellation that doesn’t happen) are more costly or disruptive than false negatives (missing a cancellation).
Low Recall: The model misses a significant portion of actual cancellations. This could be critical if missing out on actual cancellations is undesirable. For example, if the hotel depends on these predictions for managing bookings and logistics, a low recall might lead to inefficiencies.
Moderate F1 Score: This score reflects a balance between precision and recall but indicates that there is a need for improvement in both metrics.
Future Steps
Feature Engineering: Review and potentially add new features that could improve the model’s performance, such as guest demographics, seasonal factors, or historical booking trends.
Model Tuning: Adjust the model’s parameters or try different modeling techniques to see if the performance can be improved.
Data Quality: Ensure the quality of the data used for training the model. Inaccurate or biased data can significantly impact the model’s performance.
Advanced Models: Consider exploring more complex models or machine learning techniques which might capture the nuances in the data better.
Summary:
Booking cancellation model makes it possible for management to avoid potential cancellations with strategies such as discounts or other perks.
The significant difference in lead times suggests that bookings made well in advance are more likely to be canceled than those made closer to the arrival date.
bookings with more special requests are less likely to be canceled.
certain types of special requests are more influential in preventing cancellations or if the effect varies by other factors like season, hotel type, or customer segment.
The model has moderate accuracy and a balanced but moderate F1 Score. This indicates an overall decent performance but highlights potential areas for improvement, particularly in recall.
The model is more effective at avoiding false positives than at capturing all true positives (actual cancellations). Depending on the specific operational needs and costs associated with false negatives (missed cancellations) versus false positives (wrongly predicted cancellations), different strategies may be required to fine-tune the model.
Future steps could involve exploring different feature sets, trying out other modeling techniques, or adjusting the current model’s parameters to improve recall without significantly sacrificing precision.