{"id":22430017,"url":"https://github.com/brevex/hotel-booking-demand-data-analysis","last_synced_at":"2026-05-08T15:38:22.222Z","repository":{"id":210480117,"uuid":"726654599","full_name":"Brevex/Hotel-Booking-Demand-Data-Analysis","owner":"Brevex","description":"Data analysis in Python of demand for urban hotels and resorts showing their causes and relationships","archived":false,"fork":false,"pushed_at":"2024-04-10T17:30:32.000Z","size":2417,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T07:14:06.294Z","etag":null,"topics":["data-analysis","data-science","hotel-booking-analysis","kaggle","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Brevex.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-12-03T01:09:42.000Z","updated_at":"2023-12-04T01:08:56.000Z","dependencies_parsed_at":"2024-04-10T18:55:18.219Z","dependency_job_id":null,"html_url":"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis","commit_stats":null,"previous_names":["brevex/hotel-booking-demand-data-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Brevex/Hotel-Booking-Demand-Data-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FHotel-Booking-Demand-Data-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FHotel-Booking-Demand-Data-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FHotel-Booking-Demand-Data-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FHotel-Booking-Demand-Data-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Brevex","download_url":"https://codeload.github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Brevex%2FHotel-Booking-Demand-Data-Analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261897031,"owners_count":23226649,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","hotel-booking-analysis","kaggle","python"],"created_at":"2024-12-05T21:06:48.954Z","updated_at":"2026-05-08T15:38:22.217Z","avatar_url":"https://github.com/Brevex.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align = \"center\"\u003e Hotel Booking Demand Data Analysis \u003c/h1\u003e\u003cbr\u003e\n\n\u003ch2\u003e \u0026#128269; About the project \u003c/h2\u003e\u003cbr\u003e\n\n\u003cp\u003eA data analysis in Python of demand for urban hotels and resorts showing their causes and relationships.\nThe analysis makes a comparison of hotel types in Portugal, showing periods with higher\ndemand, number of canceled reservations and origin of customers\u003c/p\u003e\u003cbr\u003e\n\n\u003ch2\u003e \u0026#128302; Technologies Used \u003c/h2\u003e\u003cbr\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://github.com/syvixor/skills-icons\"\u003e\n\t  \u003cimg src=\"https://skills.syvixor.com/api/icons?i=python\" alt=\"Skills\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\u003cbr\u003e\u003ch2\u003e \u0026#128202; Analysis Result \u003c/h2\u003e\u003cbr\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003edatabase columns\u003c/summary\u003e\u003cbr\u003e\n\t\n  \t01 : hotel\n\t02 : is_canceled\n\t03 : lead_time\n\t04 : arrival_date_year\n\t05 : arrival_date_month\n\t06 : arrival_date_week_number\n\t07 : arrival_date_day_of_month\n\t08 : stays_in_weekend_nights\n\t09 : stays_in_week_nights\n\t10 : adults\n\t11 : children\n\t12 : babies\n\t13 : meal\n\t14 : country\n\t15 : market_segment\n\t16 : distribution_channel\n\t17 : is_repeated_guest\n\t18 : previous_cancellations\n\t19 : previous_bookings_not_canceled\n\t20 : reserved_room_type\n\t21 : assigned_room_type\n\t22 : booking_changes\n\t23 : deposit_type\n\t24 : agent\n\t25 : company\n\t26 : days_in_waiting_list\n\t27 : customer_type\n\t28 : adr\n\t29 : required_car_parking_spaces\n\t30 : total_of_special_requests\n\t31 : reservation_status\n\t32 : reservation_status_date\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003emissing values percentage\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eAfter importing the data, missing values were identified in some columns\u003c/p\u003e\n\t\n  \tchildren: 0.003% \n\tcountry: 0.409% \n\tagent: 13.686% \n\tcompany: 94.307%\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003enull values\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eNumber of null values per column\u003c/p\u003e\n\t\n  \tchildren: 4\n\tcountry: 488\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003ebooking percentage\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eBooking percentage by hotel type\u003c/p\u003e\n \t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/73f28799915b4907a54496bf01a4c79b8b4c743e/readme%20images/booking%20percentage.png\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003ecomparison between canceled and non-cancelled bookings\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eComparison between the number of canceled and non-cancelled reservations in each type of hotel\u003c/p\u003e\n \t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/73f28799915b4907a54496bf01a4c79b8b4c743e/readme%20images/cancelled%20bookings%20comparison.png\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003eannual evolution of bookings\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eEvolution of the number of reservations between 2015 and 2017. Note that only the year 2016 has data for all months\u003c/p\u003e\n \t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/73f28799915b4907a54496bf01a4c79b8b4c743e/readme%20images/annual%20bookings.png\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003ecustomers origin\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eList of the 10 countries where the most customers come from. Note that Portugal is 1st because the hotels in the database are in Portugal.\u003c/p\u003e\n\n  \t1\tPRT\t21071\n\t2\tGBR\t9676\n\t3\tFRA\t8481\n\t4\tESP\t6391\n\t5\tDEU\t6069\n\t6\tIRL\t2543\n\t7\tITA\t2433\n\t8\tBEL\t1868\n\t9\tNLD\t1717\n\t10\tUSA\t1596\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003emonths with more demand\u003c/summary\u003e\u003cbr\u003e\n\t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/73f28799915b4907a54496bf01a4c79b8b4c743e/readme%20images/busiest%20months.png\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003eperiod of highest price\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eMonths when prices are higher. Note that demand for resorts increases significantly during the European summer, making the price increase.\u003c/p\u003e\n\t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/73f28799915b4907a54496bf01a4c79b8b4c743e/readme%20images/price%20per%20month.png\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003eaverage cancellation per month\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eaverage 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.\u003c/p\u003e\n\t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/73f28799915b4907a54496bf01a4c79b8b4c743e/readme%20images/average%20cancellation.png\"\u003e\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\t\u003csummary\u003eheatmap\u003c/summary\u003e\u003cbr\u003e\n\t\u003cp\u003eheat map that aims to relate causes and relationships of certain values in the database.\u003c/p\u003e\n\t\u003cimg src=\"https://github.com/Brevex/Hotel-Booking-Demand-Data-Analysis/blob/2f5ff87f4032fc4d1483167686796d6aca870696/readme%20images/heatmap.png\"\u003e\n\u003c/details\u003e\n\n\u003cbr\u003e\u003ch3 align = \"center\"\u003e - By \u003ca href = \"https://www.linkedin.com/in/breno-barbosa-de-oliveira-810866275/\" target = \"_blank\"\u003eBreno\u003c/a\u003e - \u003c/h3\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrevex%2Fhotel-booking-demand-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrevex%2Fhotel-booking-demand-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrevex%2Fhotel-booking-demand-data-analysis/lists"}