This dataset is originated from <2020년 관광숙박업 등록현황> collected by Ministry of Culture, Sports and Tourism, South Korea. This data is collected at 2020, and by simple data preprocessing, I manage to obtained some basic hotel related features. Unfortunately, most observations were very messy, and I have to drop a lot of observations.

The descriptions about the columns are as follows.

• loc: Location name in Korean -width: The total width of the hotel (including non-guest room areas and floors) in square meters. -rank: The official rank(e.g. five star, four star) of the hotel determined by the Korean hotel ranking officials. -height: The number of floors in the building. -rooms: The total number of rooms. -averageWidth: width / rooms.

The Hotel Room Booking & Customer Orders Dataset This is a rich, synthetic dataset meticulously designed for data analysts, data scientists, and machine learning practitioners to practice their skills on realistic e-commerce data. It models a hotel booking platform, providing a comprehensive and interconnected environment to analyze booking trends, customer behavior, and operational patterns. It is an ideal resource for building a professional portfolio project from initial exploratory data analysis to advanced predictive modeling.

The dataset is structured as a relational database, consisting of three core tables that can be easily joined:

rooms.csv: This table serves as the hotel's inventory, containing a catalog of unique rooms with essential attributes such as room_id, type, capacity, and price_per_night.

customers.csv: This file provides a list of unique customers, offering demographic insights with columns like customer_id, name, country, and age. This data can be used to segment customers and personalize marketing strategies.

orders.csv: As the central transactional table, it links rooms and customers, capturing the details of each booking. Key columns include order_id, customer_id, room_id, booking_date, and the order_total, which can be derived from the room price and the duration of the stay.

This dataset is valuable because its structure enables a wide range of analytical projects. The relationships between tables are clearly defined, allowing you to practice complex SQL joins and data manipulation with Pandas. The presence of both categorical data (room_type, country) and numerical data (age, price) makes it versatile for different analytical approaches.

Use Cases for Data Exploration & Modeling This dataset is a versatile tool for a wide range of analytical projects:

Data Visualization: Create dashboards to analyze booking trends over time, identify the most popular room types, or visualize the geographical distribution of your customer base.

Machine Learning: Build a regression model to predict the order_total based on room type and customer characteristics. Alternatively, you could develop a model to recommend room types to customers based on their past orders.

SQL & Database Skills: Practice complex queries to find the average order value per country, or identify the most profitable room types by month.

As of September 2024, hotels in London had the highest revenue per available room (RevPAR) among cities in the United Kingdom. Based on data from the previous 12 months, the RevPAR of London hotels stood at ****** British pounds.

Data on the number of hotel rooms by district in Hong Kong in the past five years

Number of Hotel Rooms: Delhi data was reported at 9,060.000 Unit in 2020. This records a decrease from the previous number of 10,927.000 Unit for 2019. Number of Hotel Rooms: Delhi data is updated yearly, averaging 8,884.500 Unit from Dec 2004 (Median) to 2020, with 14 observations. The data reached an all-time high of 13,715.000 Unit in 2010 and a record low of 6,931.000 Unit in 2005. Number of Hotel Rooms: Delhi data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHA004: Number of Hotel Rooms: by States.

This collection covers internal tourism, in other words tourism flows within the country (domestic tourism) or from abroad to destinations in the country (inbound tourism). It only covers flows by tourist who stay at rented accommodation (with limitations of the scope, see further), and is therefore also known as "accommodation statistics".

Alternatively, this part of tourism statistics is sometimes referred to as "the supply side".

Accommodation statistics are a key part of the system of tourism statistics in the EU and have a long history of data collection. Annex I of Regulation (EU) 692/2011 of the European Parliament and of the Council deals with accommodation statistics and includes 4 sections focusing on accommodation statistics, of which sections 1 and 2 include the requirements concerning rented accommodation (capacity and occupancy respectively).

Data are collected by the competent national authorities of the Member States (generally the national statistical institute) and are compiled according to harmonised concepts and definitions and recommended methodological guidelines, before transmission to Eurostat. Most countries collect the data via sample or census surveys, sometimes in an automated manner. However, in a few cases data are compiled from a demand-side perspective (i.e. via visitor surveys or border surveys). Surveys on the occupancy of accommodation establishments are generally conducted on a monthly basis.

The concepts and definitions used in the collection of data are backed by the specifications described in the Methodological manual for tourism statistics.

Accommodation statistics comprise the following information:

Monthly data on tourism industries (NACE 55.1, 55.2 and 55.3)

Monthly occupancy of tourist accommodation establishments: arrivals and nights spent by residents and non-residents. Since reference year 2020, monthly data on nights spent is also available at NUTS 2 regional level (this series is transmitted by the Member States once per year).

Net occupancy rate of bed-places and bedrooms in hotels and similar accommodation

Annual data on tourism industries (NACE 55.1, 55.2 and 55.3)

Occupancy of tourist accommodation establishments: arrivals and nights spent by residents and non-residents

Capacity of tourist accommodation establishments: number of establishments, bedrooms and bed places

Regional data

Annual occupancy (arrivals and nights spent by residents and non-residents) of tourist accommodation establishments at NUTS 2 level (broken down by month), at NUTS 3 level, by degree of urbanisation and by coastal/non-coastal area, and for selected cities. Some indicators are only available for nights spent, not for arrivals.

Annual data on number of establishments, bedrooms and bed places at NUTS 2 level, by degree of urbanisation and by coastal/non-coastal area

Data on number of establishments, bedrooms and bed places are available by activity at NUTS 3 level until 2011.

Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

Number of Hotel Rooms: Madhya Pradesh data was reported at 1,313.000 Unit in 2020. This records an increase from the previous number of 1,283.000 Unit for 2019. Number of Hotel Rooms: Madhya Pradesh data is updated yearly, averaging 1,298.000 Unit from Dec 2004 (Median) to 2020, with 14 observations. The data reached an all-time high of 2,840.000 Unit in 2010 and a record low of 920.000 Unit in 2012. Number of Hotel Rooms: Madhya Pradesh data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHA004: Number of Hotel Rooms: by States.

According to data from the UNWTO, there were around ***** hotels and similar establishments in Australia in 2022. This reflected an increase from approximately ***** hotels and similar establishments across the country in 2013. Regional variations in hotel performance As of December 2024, the Gold Coast and Sydney recorded the highest average daily rates for hotel rooms across the country’s key hotel markets at *** Australian dollars per night, while Canberra offered comparatively more affordable options. Revenue per available room (RevPAR) also varied widely across Australia’s key cities and regions, with Sydney’s hotels achieving the highest RevPARs. Occupancy rates further highlight these regional disparities, with Perth leading with a ** percent hotel occupancy rate and Darwin trailing with an average rate of ** percent. Major players in the Australian hotel market The Australian and New Zealand hotel markets are dominated by a few key players, with Accor maintaining a significant lead. As of the 2024 financial year, Accor operated over *** travel accommodation properties in the Australasia region, more than double the next largest operator, Ascott, which managed just over *** properties. Accor's dominance is further evidenced by its hotel room inventory, boasting over ****** rooms. The next closest competitor, IHG, had approximately ****** rooms, highlighting the substantial gap between the market leader and other major hotel operators in the region.

Number of Hotel Rooms: Kerala: Four-Star data was reported at 6,420.000 Unit in 2020. This records a decrease from the previous number of 7,726.000 Unit for 2019. Number of Hotel Rooms: Kerala: Four-Star data is updated yearly, averaging 1,786.500 Unit from Dec 2004 (Median) to 2020, with 14 observations. The data reached an all-time high of 7,726.000 Unit in 2019 and a record low of 707.000 Unit in 2005. Number of Hotel Rooms: Kerala: Four-Star data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHA004: Number of Hotel Rooms: by States.

Royalton Park Avenue, located in the heart of Midtown Manhattan, offers luxurious accommodations in its stylish and modern hotel. With 249 rooms and suites, most featuring Juliet balconies and five-fixture bathrooms, guests can fully appreciate the city's vibrant atmosphere. The hotel's central location, nestled on the corner of Park Avenue and 29th Street, provides easy access to iconic landmarks, restaurants, and cultural attractions.

The hotel's sophisticated design, complete with a Parisian-chic lobby featuring a Roche Bobois Mah Jong sofa, creates a warm and inviting atmosphere for guests. The hotel's amenities include an on-site restaurant and bar, rooftop lounge, and fitness center, providing everything needed for a comfortable and indulgent stay. Whether traveling for business or leisure, Royalton Park Avenue provides a unique and memorable experience in one of the world's greatest cities.

Number of Hotel Rooms: Kerala data was reported at 16,750.000 Unit in 2020. This records a decrease from the previous number of 17,904.000 Unit for 2019. Number of Hotel Rooms: Kerala data is updated yearly, averaging 11,114.000 Unit from Dec 2004 (Median) to 2020, with 13 observations. The data reached an all-time high of 17,904.000 Unit in 2019 and a record low of 3,799.000 Unit in 2013. Number of Hotel Rooms: Kerala data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHA004: Number of Hotel Rooms: by States.

FabHotels, a leading budget hotel chain in India, has established itself as a preferred choice for travelers through its extensive network of hotels across 76+ cities. With over 1800 hotels in its portfolio, FabHotels offers comfortable and economical stays to its guests, ensuring a hassle-free experience. The company's focus on quality and cleanliness is evident in its rigorous housekeeping procedures and strict auditing processes.

FabHotels' commitment to comfort and convenience is reflected in its state-of-the-art technology and user-friendly interface. Guests can book hotels online or through the company's mobile app, and enjoy free Wi-Fi, pay-at-hotel policy, and flexible cancellation options. With over 36000 rooms and a guest base of 4 million, FabHotels has established itself as one of the largest and most preferred hotel chains in India, offering an unparalleled experience to its guests.

Ebit Time Series for Apple Hospitality REIT Inc. Apple Hospitality REIT, Inc. (NYSE: APLE) is a publicly traded real estate investment trust ("REIT") that owns one of the largest and most diverse portfolios of upscale, rooms-focused hotels in the United States. Apple Hospitality's portfolio consists of 220 hotels with approximately 29,700 guest rooms located in 85 markets throughout 37 states and the District of Columbia. Concentrated with industry-leading brands, the Company's hotel portfolio consists of 96 Marriott-branded hotels, 118 Hilton-branded hotels, five Hyatt-branded hotels and one independent hotel.

Total-Current-Liabilities Time Series for Apple Hospitality REIT Inc. Apple Hospitality REIT, Inc. (NYSE: APLE) is a publicly traded real estate investment trust ("REIT") that owns one of the largest and most diverse portfolios of upscale, rooms-focused hotels in the United States. Apple Hospitality's portfolio consists of 220 hotels with approximately 29,800 guest rooms located in 85 markets throughout 37 states and the District of Columbia. Concentrated with industry-leading brands, the Company's hotel portfolio consists of 97 Marriott-branded hotels, 117 Hilton-branded hotels, five Hyatt-branded hotels and one independent hotel.

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data .xlsx file.

This files containing data on 2019 Airbnb listings are available to you:

data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing description: listing description room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments

Consent Management for Hotel Guests Market Outlook

According to our latest research, the global Consent Management for Hotel Guests market size reached USD 1.28 billion in 2024 and is set to expand at a robust CAGR of 13.7% through the forecast period, with the market projected to reach USD 3.84 billion by 2033. This impressive growth is primarily driven by increasing regulatory scrutiny around data privacy, a surge in digital transformation initiatives within the hospitality sector, and the rising demand for personalized guest experiences. As per our most recent analysis, the market is witnessing accelerated adoption of consent management solutions as hotels strive to meet evolving compliance requirements and build guest trust.

One of the most significant growth factors propelling the Consent Management for Hotel Guests market is the escalating complexity of global data privacy regulations. With the implementation of stringent frameworks such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar legislations across Asia Pacific and other regions, hotels are now compelled to adopt robust systems to capture, store, and manage guest consent. Failure to comply can result in hefty penalties and reputational damage. This regulatory landscape is pushing both large hotel chains and independent properties to invest in advanced consent management platforms that offer automation, transparency, and auditability. Furthermore, the growing awareness among travelers regarding their data rights is making consent management a critical differentiator for hotels aiming to foster guest loyalty and trust.

Another crucial driver is the increasing digitalization of the hospitality sector. As hotels embrace digital technologies for booking, marketing, guest engagement, and operations, the volume and sensitivity of guest data being collected have surged. This digital transformation necessitates sophisticated consent management solutions that can seamlessly integrate with various hotel management systems and digital touchpoints. Modern guests interact with hotels through websites, mobile apps, kiosks, and even IoT-enabled devices within rooms, each requiring explicit and traceable consent for data collection and usage. The need to unify and manage these consent records across disparate systems is spurring demand for interoperable consent management solutions that ensure compliance without compromising guest experience.

Personalization has emerged as a key trend in the hospitality industry, with hotels leveraging guest data to tailor services, offers, and communications. However, this drive toward hyper-personalization is intricately linked with the need for transparent consent management. Guests are more likely to share personal preferences and behavioral data when assured of privacy and control. As a result, consent management platforms are evolving to offer granular consent options, allowing guests to select what data they wish to share and for what purposes. This not only enhances guest satisfaction but also enables hotels to derive actionable insights while remaining within the bounds of data privacy regulations. The interplay between personalization and privacy is thus a central theme shaping the future of consent management in the hospitality sector.

From a regional perspective, North America and Europe are currently leading the market, driven by early adoption of privacy regulations and advanced hospitality infrastructure. However, Asia Pacific is exhibiting the fastest growth, fueled by rapid digitalization, increasing international travel, and the proliferation of luxury and boutique hotels. The Middle East and Latin America are also witnessing gradual uptake, particularly among upscale hotels seeking to differentiate themselves through superior guest privacy standards. As global travel rebounds post-pandemic, the need for cross-border data compliance and seamless guest consent management is expected to intensify, further expanding the market's reach and complexity across all regions.

Booking.com is one of the world's leading online travel agencies, offering a vast selection of accommodation options, including hotels, apartments, and vacation homes, in over 220 countries and territories worldwide.

From Booking.com, I have collected hotel information including their names, ratings, reviews, and locations.

The hotels on Booking.com vary in their ratings, ranging from one to five stars. Some of the highest-rated hotels have received excellent feedback from guests and are considered to be among the best in their respective areas.

The reviews for these hotels are mostly positive, with guests praising the amenities, location, and overall experience. Many guests have highlighted the comfort and spaciousness of the rooms, the quality of the service, and the friendliness of the staff.

The hotels are located in various settings, with some located in the heart of bustling cities while others are situated in serene and picturesque locations. For example, some hotels offer easy access to popular tourist attractions, while others provide a peaceful retreat away from the hustle and bustle of city life.

Booking.com offers a wide selection of hotels with great ratings, reviews, and locations to choose from, making it a valuable resource for travelers who are looking for the ideal accommodation for their next trip.

This collection covers internal tourism, in other words tourism flows within the country (domestic tourism) or from abroad to destinations in the country (inbound tourism). It only covers flows by tourist who stay at rented accommodation (with limitations of the scope, see further), and is therefore also known as "accommodation statistics".

Alternatively, this part of tourism statistics is sometimes referred to as "the supply side".

Accommodation statistics are a key part of the system of tourism statistics in the EU and have a long history of data collection. Annex I of Regulation (EU) 692/2011 of the European Parliament and of the Council deals with accommodation statistics and includes 4 sections focusing on accommodation statistics, of which sections 1 and 2 include the requirements concerning rented accommodation (capacity and occupancy respectively).

Data are collected by the competent national authorities of the Member States (generally the national statistical institute) and are compiled according to harmonised concepts and definitions and recommended methodological guidelines, before transmission to Eurostat. Most countries collect the data via sample or census surveys, sometimes in an automated manner. However, in a few cases data are compiled from a demand-side perspective (i.e. via visitor surveys or border surveys). Surveys on the occupancy of accommodation establishments are generally conducted on a monthly basis.

The concepts and definitions used in the collection of data are backed by the specifications described in the Methodological manual for tourism statistics.

Accommodation statistics comprise the following information:

Monthly data on tourism industries (NACE 55.1, 55.2 and 55.3)

Monthly occupancy of tourist accommodation establishments: arrivals and nights spent by residents and non-residents. Since reference year 2020, monthly data on nights spent is also available at NUTS 2 regional level (this series is transmitted by the Member States once per year).

Net occupancy rate of bed-places and bedrooms in hotels and similar accommodation

Annual data on tourism industries (NACE 55.1, 55.2 and 55.3)

Occupancy of tourist accommodation establishments: arrivals and nights spent by residents and non-residents

Capacity of tourist accommodation establishments: number of establishments, bedrooms and bed places

Regional data

Annual occupancy (arrivals and nights spent by residents and non-residents) of tourist accommodation establishments at NUTS 2 level (broken down by month), at NUTS 3 level, by degree of urbanisation and by coastal/non-coastal area, and for selected cities. Some indicators are only available for nights spent, not for arrivals.

Annual data on number of establishments, bedrooms and bed places at NUTS 2 level, by degree of urbanisation and by coastal/non-coastal area

Data on number of establishments, bedrooms and bed places are available by activity at NUTS 3 level until 2011.

Please note that for paragraphs where no metadata for regional data has been specified, the regional metadata is identical to the metadata provided for the national data.

This dataset provides a snapshot of the Airbnb market in Albany, New York, as of September 5, 2024. Airbnb is a global platform that connects hosts with guests seeking short-term accommodations, offering diverse lodging options ranging from entire homes and apartments to single rooms and shared spaces. Albany, as the capital of New York State, has a unique Airbnb market shaped by its mix of government-related travel, tourism, and local events.

The data includes three files – calendar.csv, listings.csv, and reviews.csv – which together capture key aspects of Albany’s Airbnb ecosystem:

Listings: Details on property types, pricing, and host characteristics.

Calendar: Daily availability and pricing data.

Reviews: Guest feedback on individual listings.

Overall Structure & Analysis Potential:

This dataset offers a rich foundation for analyzing various aspects of the Albany Airbnb market, such as:

Availability & Pricing Trends: By combining calendar.csv and listings.csv, one can examine how pricing and availability fluctuate over time and across different types of listings.

Listing Characteristics: listings.csv provides an overview of the type, size, and features of listings in Albany, helping identify the most common accommodation types and pricing ranges.

Guest Feedback: reviews.csv allows for sentiment analysis and topic modeling to understand guest satisfaction and common issues.

Seychelles Ecosystem Services: On-Reef Tourism Seychelles is highly dependent on coastal and marine tourism activities, many of them associated with coral reefs, either directly (“on-reef” e.g., SCUBA, snorkeling) or indirectly (e.g., beach-related activities, access to fresh seafood). While previous studies have quantified and mapped the value of coral reefs to tourism at the global scale (Spalding et al., 2017), downscaling these analyses to the regional and local levels afford an opportunity to integrate emerging artificial intelligence and machine learning (AI/ML) technologies, incorporate data from local sources, and engage with stakeholders who can guide additional refinements to the methodologies. Under this project, TNC improved its global estimates of on-reef tourism expenditure and visitation estimates by integrating fine-scale benthic habitat data, cross-referencing global tourism datasets with local sources of information on dive sites, dive shops, and hotels, and applying AI/ML methodologies to photos and reviews kindly provided by TripAdvisor to further highlight patterns of reef-related tourism. This dataset will enable a broad range of users from the public to industry to government to better plan and manage both the tourism industry and any other active sectors within the blue economy. Model Inputs (see technical report for full description regarding how these inputs were used to develop the model outputs)

Data input

Source(s)

Model Treatment

Photo User Days (Underwater Photos)

Flickr; Spalding et al. 2017

Buffered by 1km

Reviews of attractions featuring on-reef activities

TripAdvisor

Used in calculation of proportion of tourists enjoying on-reef activities

Dive Sites

Diveboard, MEECC, TrekDives, DiveAdvisor, Dive Seychelles, Big Blue Divers, Okeanos-Cruise, Dive.Site, Blue Safaris

Weighted by popularity; Buffered by 1km

Dive Shops

Diveboard, TripAdvisor, Diveary

Used in calculation of proportion of tourists enjoying on-reef activities

Hotels

GARD, MHILT

Used in calculation of proportion of tourists enjoying on-reef activities

Coral Reef Habitat

Allen Coral Atlas, Klaus 2015

Coral/Algae class extracted, merged with areas of granitic reef and additional buffered dive sites; gridded to 100m raster

Tourism Arrivals & Expenditures

Government of Seychelles National Bureau of Statistics

% driven by on-reef tourism calculated via proxy indicators

Model Outputs On-Reef Tourism Expenditures: The On-Reef Tourism Expenditures model provides mapped estimates of the annual dollar expenditure and contribution of coral reefs to the tourism sector from on-reef tourism activities such as snorkeling and SCUBA. Values were assigned based on reef use intensity determined by proximity to dive sites and underwater photography. Values are expressed in $USD, per hectare, per year. On-Reef Tourism Intensity: Photo images and dive-sites were both used to generate a weighted map of the intensity of on-reef tourism activities to get a measure of a cultural ecosystem service. To create a weighted map, each point location of # of PUD images or dive intensity was buffered by 1km, and the total reef area within that 1 km radius was calculated for each point. Each point’s score (# of PUDs or dive intensity) was then divided by the total area of the buffer to spread the intensity based on the total area of reef tract. A point density analysis was then performed on each of these input layers based on that value and these two layers were summed to provide an intensity score which incorporated overlapping scores where PUDs and/or dive sites were generating overlapping scores. This layer was then clipped to the map of coral reefs such that every 100m tract of reef received a unitless use-intensity score. On-Reef Visitation Value: A series of indicators were developed to give a clear indication of the proportion of persons enjoying on-reef activities or their equivalent spending. These indicators were drawn from visitor exit surveys (National Bureau of Statistics, 2017); the ratio of underwater PUDs to total Flickr photos; and the ratio of dive shops (derived from DiveBoard and supplemented with data from TripAdvisor and Diveary) to the number of hotel rooms (Global Accommodation Reference Database cross-referenced to data provided by Ministry for Habitat, Infrastructure and Land Transport (MHILT)). Using this approach, comparing these indicators to similar indicators from around the world and informed by further academic studies on the relative importance of on-reef activities in certain countries, the team determined that the value of on reef tourism activities on coral reefs, for overall tourism in the Seychelles, was 9% of the annual tourism expenditures and visitor arrivals. The values for coral-reef associated arrivals and expenditures were distributed across the coral reefs weighted by the intensity maps to arrive at the final version of the maps, in which each 100m tract of coral reef has an associated tourism expenditure and visitation value. Model Output Datasets On-Reef Tourism Expenditures Dataset name: Sey_on_reef_tourism_expendituresDataset type: ESRI File Geodatabase raster, floating pointRaster values: Values represent $USD attributed to on-reef tourism, per hectare, per year. On-Reef Tourism VisitationDataset name: Sey_on_reef_visitation_valueDataset type: ESRI File Geodatabase raster, floating point Raster values: Values represent number of visitors engaging on on-reef tourism, per hectare, per year. On-Reef Tourism Intensity Dataset name: Sey_on_reef_tourism_intensityDataset type: ESRI File Geodatabase raster, floating pointRaster values: Every 100m tract of reef (100m grid cell) received a unitless use-intensity score, representing lowest to highest on-reef tourism use intensity. References: Flickr User Data Government of Seychelles, MACCE database National Bureau of Statistics (2017). Visitor Expenditure Survey. https://www.nbs.gov.sc/downloads/economic-statistics/visitor-expenditure-survey/2017 Spalding, M. D., L. Burke, S. Wood, J. Ashpole, J. Hutchison, and P. z. Ermgassen. 2017. Mapping the global value and distribution of coral reef tourism. Marine Policy 82:104-113.