Hotel Bookings Analysis in R

Analyzing Hotel Booking Patterns in R Programming Langauge involves examining a dataset related to hotel bookings to derive insights into booking trends, customer behavior, and other relevant factors. In this case, the dataset contains columns such as hotel type, cancellation status, lead time, arrival date details, and other booking-related information.

Dataset Link:- Hotel Booking Demand

Load Packages and Data

• First, install and load the required packages.
• Next, read the Dataset and check the first few rows.

Output:

 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_week_number arrival_date_day_of_month stays_in_weekend_nights
1 27 1 0
2 27 1 0
3 27 1 0
4 27 1 0
5 27 1 0
6 27 1 0
 stays_in_week_nights adults children babies meal country market_segment
1 0 2 0 0 BB PRT Direct
2 0 2 0 0 BB PRT Direct
3 1 1 0 0 BB GBR Direct
4 1 1 0 0 BB GBR Corporate
5 2 2 0 0 BB GBR Online TA
6 2 2 0 0 BB GBR Online TA
 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
 previous_bookings_not_canceled reserved_room_type assigned_room_type
1 0 C C
2 0 C C
3 0 A C
4 0 A A
5 0 A A
6 0 A A
 booking_changes deposit_type agent company days_in_waiting_list customer_type
1 3 No Deposit NULL NULL 0 Transient
2 4 No Deposit NULL NULL 0 Transient
3 0 No Deposit NULL NULL 0 Transient
4 0 No Deposit 304 NULL 0 Transient
5 0 No Deposit 240 NULL 0 Transient
6 0 No Deposit 240 NULL 0 Transient
 adr required_car_parking_spaces total_of_special_requests reservation_status
1 0 0 0 Check-Out
2 0 0 0 Check-Out
3 75 0 0 Check-Out
4 75 0 0 Check-Out
5 98 0 1 Check-Out
6 98 0 1 Check-Out
 reservation_status_date
1 2015-07-01
2 2015-07-01
3 2015-07-02
4 2015-07-02
5 2015-07-03
6 2015-07-03

Check the Structure

Let's check the structure of the dataset .

Output:

'data.frame': 119390 obs. of 32 variables:
 $ hotel : chr "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
 $ is_canceled : int 0 0 0 0 0 0 0 0 1 1 ...
 $ lead_time : int 342 737 7 13 14 14 0 9 85 75 ...
 $ arrival_date_year : int 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
 $ arrival_date_month : chr "July" "July" "July" "July" ...
 $ arrival_date_week_number : int 27 27 27 27 27 27 27 27 27 27 ...
 $ arrival_date_day_of_month : int 1 1 1 1 1 1 1 1 1 1 ...
 $ stays_in_weekend_nights : int 0 0 0 0 0 0 0 0 0 0 ...
 $ stays_in_week_nights : int 0 0 1 1 2 2 2 2 3 3 ...
 $ adults : int 2 2 1 1 2 2 2 2 2 2 ...
 $ children : int 0 0 0 0 0 0 0 0 0 0 ...
 $ babies : int 0 0 0 0 0 0 0 0 0 0 ...
 $ meal : chr "BB" "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 : int 0 0 0 0 0 0 0 0 0 0 ...
 $ previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
 $ previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
 $ reserved_room_type : chr "C" "C" "A" "A" ...
 $ assigned_room_type : chr "C" "C" "C" "A" ...
 $ booking_changes : int 3 4 0 0 0 0 0 0 0 0 ...
 $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
 $ agent : chr "NULL" "NULL" "NULL" "304" ...
 $ company : chr "NULL" "NULL" "NULL" "NULL" ...
 $ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
 $ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
 $ adr : num 0 0 75 75 98 ...
 $ required_car_parking_spaces : int 0 0 0 0 0 0 0 0 0 0 ...
 $ total_of_special_requests : int 0 0 0 0 1 1 0 1 1 0 ...
 $ reservation_status : chr "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
 $ reservation_status_date : chr "2015-07-01" "2015-07-01" "2015-07-02" "2015-07-02" ...

Check the Summary

Checking the summary of the dataset it provides various information like( min , meadian , mode ,etc)

Output:

 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 children 
 Min. : 0.0000 Min. : 0.0 Min. : 0.000 Min. : 0.0000 
 1st Qu.: 0.0000 1st Qu.: 1.0 1st Qu.: 2.000 1st Qu.: 0.0000 
 Median : 1.0000 Median : 2.0 Median : 2.000 Median : 0.0000 
 Mean : 0.9276 Mean : 2.5 Mean : 1.856 Mean : 0.1039 
 3rd Qu.: 2.0000 3rd Qu.: 3.0 3rd Qu.: 2.000 3rd Qu.: 0.0000 
 Max. :19.0000 Max. :50.0 Max. :55.000 Max. :10.0000 
 NA's :4 
 babies meal country market_segment 
 Min. : 0.000000 Length:119390 Length:119390 Length:119390 
 1st Qu.: 0.000000 Class :character Class :character Class :character 
 Median : 0.000000 Mode :character Mode :character Mode :character 
 Mean : 0.007949 
 3rd Qu.: 0.000000 
 Max. :10.000000 

 distribution_channel is_repeated_guest previous_cancellations
 Length:119390 Min. :0.00000 Min. : 0.00000 
 Class :character 1st Qu.:0.00000 1st Qu.: 0.00000 
 Mode :character Median :0.00000 Median : 0.00000 
 Mean :0.03191 Mean : 0.08712 
 3rd Qu.:0.00000 3rd Qu.: 0.00000 
 Max. :1.00000 Max. :26.00000 

 previous_bookings_not_canceled reserved_room_type assigned_room_type
 Min. : 0.0000 Length:119390 Length:119390 
 1st Qu.: 0.0000 Class :character Class :character 
 Median : 0.0000 Mode :character Mode :character 
 Mean : 0.1371 
 3rd Qu.: 0.0000 
 Max. :72.0000 

 booking_changes deposit_type agent company 
 Min. : 0.0000 Length:119390 Length:119390 Length:119390 
 1st Qu.: 0.0000 Class :character Class :character Class :character 
 Median : 0.0000 Mode :character Mode :character Mode :character 
 Mean : 0.2211 
 3rd Qu.: 0.0000 
 Max. :21.0000 

 days_in_waiting_list customer_type adr 
 Min. : 0.000 Length:119390 Min. : -6.38 
 1st Qu.: 0.000 Class :character 1st Qu.: 69.29 
 Median : 0.000 Mode :character Median : 94.58 
 Mean : 2.321 Mean : 101.83 
 3rd Qu.: 0.000 3rd Qu.: 126.00 
 Max. :391.000 Max. :5400.00 

 required_car_parking_spaces total_of_special_requests reservation_status
 Min. :0.00000 Min. :0.0000 Length:119390 
 1st Qu.:0.00000 1st Qu.:0.0000 Class :character 
 Median :0.00000 Median :0.0000 Mode :character 
 Mean :0.06252 Mean :0.5714 
 3rd Qu.:0.00000 3rd Qu.:1.0000 
 Max. :8.00000 Max. :5.0000 

 reservation_status_date
 Length:119390 
 Class :character 
 Mode :character 

Count NA Values

Checking the null values from the dataset.

Output:

Total NA values in the entire data frame: 4 
NA values per column:
 hotel is_canceled 
 0 0 
 lead_time arrival_date_year 
 0 0 
 arrival_date_month arrival_date_week_number 
 0 0 
 arrival_date_day_of_month stays_in_weekend_nights 
 0 0 
 stays_in_week_nights adults 
 0 0 
 children babies 
 4 0 
 meal country 
 0 0 
 market_segment distribution_channel 
 0 0 
 is_repeated_guest previous_cancellations 
 0 0 
previous_bookings_not_canceled reserved_room_type 
 0 0 
 assigned_room_type booking_changes 
 0 0 
 deposit_type agent 
 0 0 
 company 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 0 

Removing NA Values

If any null values is present it will be removed by this command.

Output:

[1] 0

Visualization

Barplot

The barplot shows the both type hotel reservations number .

Output:

👁 gh

Scatterplot between Lead Time vs. Average Daily Rate

This scatterplot aims to understand the relationship between lead time (the number of days between booking and arrival) and the Average Daily Rate (ADR). The points on the scatterplot represent individual bookings.

Output:

👁 gh

A positive correlation between lead time and ADR suggests that longer lead times may be associated with lower ADR.

• Clusters or patterns in the scatterplot may indicate seasonality or specific booking trends.

Histogram with Distplot for Hotel Booking Pattern in R

We take sample portion of this large dataset , it can be modified according to the demand.

Output:

👁 gh

Skewed distributions may indicate certain booking behaviors, such as last-minute bookings or extended stays.

• Peaks and valleys in the distplot offer insights into preferred booking patterns within the dataset.

Outliers Detection forHotel Booking Pattern Data in R

Outliers can significantly impact analysis. This step involves identifying and examining potential outliers in numeric columns like lead time, ADR, or stays.

Output:

👁 gh

Outliers may highlight exceptional cases, such as extremely long lead times or unusually high ADRs.

• Understanding outliers helps in assessing the robustness of general trends and patterns in the dataset.

Check Monthly Booking Trends

Analyzing booking trends on a monthly basis helps identify seasonality and understand when hotels experience peak or off-peak periods.

Output:

👁 gh

Peaks in certain months may be attributed to holidays, events, or specific seasons, influencing booking patterns.

• Understanding monthly trends aids in optimizing resource allocation and marketing strategies.

Check the Average Daily Rate Distribution

Examining the distribution of Average Daily Rates provides insights into the pricing strategy of hotels.

Output:

👁 gh

Peaks or clusters in the ADR distribution may indicate different pricing tiers or packages.

• Understanding ADR distribution helps in setting competitive and profitable pricing strategies.

Check Market Segment Distribution

Analyzing the distribution of market segments provides insights into the customer base and booking sources.

Output:

👁 gh

Dominant market segments indicate the primary sources of bookings, influencing marketing and communication strategies.

• Varied market segment distribution helps in understanding the diverse customer base.

Check Most Preffered hotel

Identifying the most preferred hotel provides insights into customer preferences and popularity.

Output:

👁 gh

The preferred city hotel may have unique features or services that attract more bookings.

• Understanding customer preferences aids in targeted marketing and service improvements.

Visualize Cancellation Patterns

Visualizing cancellation patterns helps in understanding factors influencing cancellations and optimizing reservation management.

Output:

👁 gh

Peaks in cancellation rates may be linked to specific seasons, events, or external factors.

• Identifying patterns in cancellations assists in implementing strategies to reduce cancellations.

Analyzing Booking Distribution by Month

Examining the booking distribution by month provides insights into booking patterns over the entire dataset.

Output:

👁 gh

Seasonal variations may be observed, influencing marketing and pricing strategies.

• Identifying booking patterns helps in resource planning and optimizing hotel operations.

Check the Customer type

Analyze the distribution of bookings based on customer types (Transient, Contract, Group, etc.).

Output:

👁 gh

The barplot visualizes the proportion of bookings attributed to different customer types, such as Transient, Contract, Group, etc.

• It helps identify which customer segments contribute significantly to the overall bookings, allowing for targeted marketing efforts or tailored services.
• Understanding the preferred customer types can guide strategies to attract and retain specific segments, enhancing customer satisfaction and loyalty.

Checking the Booking Channels

This barplot illustrates the distribution of bookings through different channels, providing insights into the popularity of channels such as direct bookings, online travel agencies (OTA), and corporate bookings.

Output:

👁 gh

The visualization shows the distribution of bookings across various channels such as Direct, Online Travel Agencies (OTA), and Corporate.

• From the barplot, it appears that the "TA/TO" (Travel Agents/Tour Operators) channel has the highest number of bookings compared to other channels.
• The hotel management may infer that the TA/TO channel is crucial for bookings, and they might consider strengthening marketing efforts or partnerships with travel agencies to further enhance this channel.

Correlation Heatmap of Numeric Columns

The correlation heatmap illustrates the relationships between numeric variables, revealing potential dependencies.

Output:

👁 gh

Strong correlations may indicate interdependencies between variables, influencing decision-making.

• Understanding correlations helps in identifying key factors affecting booking patterns.

Analyze the Weekday Vs Weekend bookings

Visualize the distribution of bookings between weekdays and weekends.

Output:

👁 gh

The number of bookings during weekdays is notably lower than weekends, indicating a higher preference for weekends stays.

Conclusion

This R analysis explores hotel booking patterns, covering data loading, structure check, and visualization. Various plots reveal trends, distributions, customer type , booking source and correlations, offering insights into customer behavior. The most preferred hotel and cancellation patterns are highlighted. Overall, the analysis provides valuable information for understanding and decision-making in the hospitality industry.