https://github.com/brevex/hotel-booking-demand-data-analysis

Data analysis in Python of demand for urban hotels and resorts showing their causes and relationships
https://github.com/brevex/hotel-booking-demand-data-analysis

data-analysis data-science hotel-booking-analysis kaggle python

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Data analysis in Python of demand for urban hotels and resorts showing their causes and relationships

• Host: GitHub
• URL: https://github.com/brevex/hotel-booking-demand-data-analysis
• Owner: Brevex
• Created: 2023-12-03T01:09:42.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-10T17:30:32.000Z (10 months ago)
• Last Synced: 2024-04-10T21:24:14.673Z (10 months ago)
• Topics: data-analysis, data-science, hotel-booking-analysis, kaggle, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.31 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
 Hotel Booking Demand Data Analysis 



 🔍 About the project 



A data analysis in Python of demand for urban hotels and resorts showing their causes and relationships.

The analysis makes a comparison of hotel types in Portugal, showing periods with higher

demand, number of canceled reservations and origin of customers



 🔮 Technologies Used 





  

    

  




 📊 Analysis Result 



	database columns


	

  	01 : hotel

	02 : is_canceled

	03 : lead_time

	04 : arrival_date_year

	05 : arrival_date_month

	06 : arrival_date_week_number

	07 : arrival_date_day_of_month

	08 : stays_in_weekend_nights

	09 : stays_in_week_nights

	10 : adults

	11 : children

	12 : babies

	13 : meal

	14 : country

	15 : market_segment

	16 : distribution_channel

	17 : is_repeated_guest

	18 : previous_cancellations

	19 : previous_bookings_not_canceled

	20 : reserved_room_type

	21 : assigned_room_type

	22 : booking_changes

	23 : deposit_type

	24 : agent

	25 : company

	26 : days_in_waiting_list

	27 : customer_type

	28 : adr

	29 : required_car_parking_spaces

	30 : total_of_special_requests

	31 : reservation_status

	32 : reservation_status_date

	missing values percentage


	
After importing the data, missing values were identified in some columns

	

  	children: 0.003% 

	country: 0.409% 

	agent: 13.686% 

	company: 94.307%


	null values


	
Number of null values per column

	

  	children: 4

	country: 488


	booking percentage


	
Booking percentage by hotel type

 	


	comparison between canceled and non-cancelled bookings


	
Comparison between the number of canceled and non-cancelled reservations in each type of hotel

 	


	annual evolution of bookings


	
Evolution of the number of reservations between 2015 and 2017. Note that only the year 2016 has data for all months

 	


	customers origin


	
List of the 10 countries where the most customers come from. Note that Portugal is 1st because the hotels in the database are in Portugal.


  	1	PRT	21071

	2	GBR	9676

	3	FRA	8481

	4	ESP	6391

	5	DEU	6069

	6	IRL	2543

	7	ITA	2433

	8	BEL	1868

	9	NLD	1717

	10	USA	1596

	months with more demand


	

	period of highest price


	
Months when prices are higher. Note that demand for resorts increases significantly during the European summer, making the price increase.

	


	average cancellation per month


	
average number of bookings canceled per month. Note that it is understandable that the months with the highest demand are the ones with the most cancellations.

	


	heatmap


	
heat map that aims to relate causes and relationships of certain values in the database.

	



 - By Breno -