Smart Hospitality Management Market Outlook

The global smart hospitality management market size is projected to see significant growth from 2023, valued at approximately $18.5 billion, to an anticipated $54.3 billion by 2032, reflecting a strong compound annual growth rate (CAGR) of 12.8%. The driving force behind this impressive expansion is the growing demand for efficient and personalized guest experiences in the hospitality industry. The integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing is revolutionizing how hospitality services are managed, ensuring streamlined operations and enhanced customer satisfaction. This surge is predominantly fueled by the increasing adoption of smart technologies that cater to the evolving expectations of modern-day travelers who demand seamless and personalized experiences.

One of the most significant growth factors in the smart hospitality management market is the increased emphasis on enhanced guest experiences. Today’s travelers are looking for more than just a place to stay; they want experiences that are seamless, personalized, and convenient. Smart technologies in hospitality facilitate a variety of personalized guest services, such as automated check-ins, customized room settings, and virtual concierge services. These technologies not only enhance guest satisfaction but also empower hotels to manage their resources more efficiently by reducing manual work and minimizing human errors. Furthermore, the ability to analyze customer preferences and behaviors through big data analytics enables hospitality providers to tailor their services and create a more personalized experience, which in turn drives customer loyalty and repeat business.

Another key driver in this market is the operational efficiency offered by smart hospitality solutions. With the integration of IoT, AI, and cloud-based systems, hospitality providers can significantly optimize their operations. These technologies enable real-time monitoring and management of resources, which leads to reduced costs and improved service delivery. For example, smart energy management systems help in reducing energy consumption by automating lighting and climate control based on occupancy, thereby contributing to sustainability initiatives and cost savings. Additionally, predictive maintenance enabled by IoT sensors can foresee potential equipment failures, thus minimizing downtime and extending the lifespan of assets. This operational efficiency not only enhances the bottom line of hospitality businesses but also aligns with the growing trend of sustainable and eco-friendly practices within the industry.

The burgeoning trend of technology adoption in smaller and budget hospitality establishments is also contributing to the market's growth. While luxury hotels were the early adopters of smart technologies, smaller hotels and budget accommodations are increasingly recognizing the value these systems bring. By implementing scaled-down versions of smart solutions, these establishments can improve their competitiveness without incurring the high costs associated with extensive renovations. The democratization of technology, with affordable and customizable solutions, is enabling a wider range of hospitality providers to enhance their operational capabilities and guest experiences. This trend is particularly pronounced in emerging markets where smaller establishments are looking to differentiate themselves and capture a broader customer base through improved service offerings.

Regionally, the Asia Pacific is expected to witness the highest growth rate in the smart hospitality management market. The region’s booming tourism industry, coupled with rapid urbanization and technological advancements, makes it a hotbed for smart hospitality solutions. Additionally, governmental support for smart city projects and increased investment in tourism infrastructure contribute to the robust growth in this region. The North American market, on the other hand, is characterized by early technology adoption and a strong focus on innovation, maintaining a significant share of the global market. In Europe, stringent regulations around sustainability and data privacy drive the adoption of smart solutions that comply with these standards, while Middle East & Africa see a surge in luxury tourism and mega-events, further boosting the demand for advanced hospitality technologies.

Component Analysis

In the smart hospitality management

This Hotel Dataset: Rates, Reviews & Amenities(6k+) dataset includes hotel rates, guest reviews, and available amenities from two popular travel websites, TripAdvisor and Booking.com. The dataset can be used to analyze trends and insights in the hospitality industry, and inform decisions related to pricing, marketing, and customer service. Booking.com: Founded in 1996 in Amsterdam, Booking.com has grown from a small Dutch start-up to one of the world’s leading digital travel companies. Part of Booking Holdings Inc. (NASDAQ: BKNG), Booking.com’s mission is to make it easier for everyone to experience the world.

By investing in technology that takes the friction out of travel, Booking.com seamlessly connects millions of travelers to memorable experiences, a variety of transportation options, and incredible places to stay – from homes to hotels, and much more. As one of the world’s largest travel marketplaces for both established brands and entrepreneurs of all sizes, Booking.com enables properties around the world to reach a global audience and grow their businesses.

Booking.com is available in 43 languages and offers more than 28 million reported accommodation listings, including over 6.6 million homes, apartments, and other unique places to stay. Wherever you want to go and whatever you want to do, Booking.com makes it easy and supports you with 24/7 customer support. Tripadvisor, the world's largest travel guidance platform*, helps hundreds of millions of people each month** become better travelers, from planning to booking to taking a trip. Travelers across the globe use the Tripadvisor site and app to discover where to stay, what to do and where to eat based on guidance from those who have been there before. With more than 1 billion reviews and opinions of nearly 8 million businesses, travelers turn to Tripadvisor to find deals on accommodations, book experiences, reserve tables at delicious restaurants and discover great places nearby. As a travel guidance company available in 43 markets and 22 languages, Tripadvisor makes planning easy no matter the trip type. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), own and operate a portfolio of travel media brands and businesses, operating under various websites and apps.

This dataset is about books, has 4 rows. and is filtered where the book is Sustainability in the hospitality industry : principles of sustainable operations. It features 7 columns including book, author, publication date, language, and book publisher. The preview is ordered by publication date (descending).

This dataset is about book subjects. It has 1 row and is filtered where the books is Study guide to accompany Introduction to management in the hospitality industry, ninth edition. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Utilize our Trivago dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding travel and hospitality industry dynamics and trends, empowering organizations to refine their service offerings and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements.

Popular use cases include pricing optimization, where organizations can define a pricing strategy and create dynamic pricing models by comparing similar accommodations and categories among competitors. Additionally, the dataset helps in identifying gaps in hotel inventory, recognizing increased demand for certain destinations, and spotting travel trends that are gaining popularity with consumers. Furthermore, it supports market strategy optimization by leveraging insights to analyze key travel trends and customer preferences, enhancing overall business decision-making.

This dataset is about books. It has 1 row and is filtered where the book is Purchasing and costing for the hospitality industry. It features 7 columns including author, publication date, language, and book publisher.

Bitext - Hospitality Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [hospitality] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-hospitality-llm-chatbot-training-dataset.

Success.ai’s B2B Contact Data for Global Hospitality Executives provides access to verified contact information for decision-makers shaping the hotel and hospitality industry worldwide. Sourced from over 170 million verified professional profiles, this dataset includes work emails, direct phone numbers, and LinkedIn profiles for key executives and leaders in hotels, resorts, and hospitality groups. Whether you’re targeting hotel owners, general managers, revenue directors, or operations executives, Success.ai delivers accurate, relevant, and timely data to enhance your outreach and drive business growth.

Why Choose Success.ai’s Hospitality Executives Data?

1. Comprehensive Contact Information
2. Access verified work emails, phone numbers, and LinkedIn profiles of hospitality executives worldwide.

3. AI-driven validation ensures 99% accuracy, enabling confident and efficient communication with the right individuals.

4. Global Reach Across Hospitality Segments

5. Includes profiles of hotel owners, general managers, sales directors, revenue managers, and operations leaders in hotels, resorts, and hospitality chains.

6. Covers North America, Europe, Asia-Pacific, South America, and the Middle East, ensuring a truly global perspective.

7. Continuously Updated Datasets

8. Real-time updates keep your data fresh and actionable, allowing you to engage with the most current decision-makers in the hospitality industry.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring that all outreach efforts are ethical and legally compliant.

Data Highlights:

• 170M+ Verified Professional Profiles: Includes hospitality executives and decision-makers across diverse markets.
• 50M Work Emails: AI-validated for precise, reliable outreach.
• 30M Company Profiles: Offering insights into hotel groups, hospitality brands, and independent properties.
• 700M Global Professional Profiles: Enriched data supporting a broad range of strategic initiatives.

Key Features of the Dataset:

1. Hospitality Decision-Maker Profiles
2. Identify and connect with CEOs, general managers, sales directors, and revenue managers responsible for key operational and strategic decisions in hotels and related hospitality businesses.

3. Engage with individuals who shape guest experiences, manage pricing strategies, oversee supply chains, and direct marketing efforts.

4. Advanced Filters for Precision Targeting

5. Filter contacts by region, hotel brand, property size, star rating, job title, and other criteria to tailor your outreach for maximum relevance and impact.

6. Refine campaigns to target decision-makers aligned with your product or service offerings.

7. AI-Driven Enrichment

8. Profiles are enriched with actionable data points, giving you the insights needed to personalize messaging and boost engagement rates.

Strategic Use Cases:

1. Sales and Vendor Relationships
2. Present technology solutions, guest amenities, or operational improvements to hotel owners, procurement managers, or operations leaders.

3. Build partnerships with hospitality executives seeking quality suppliers and innovative offerings.

4. Marketing and Brand Expansion

5. Target marketing and revenue directors to promote your services—such as branding, digital marketing tools, or loyalty programs—across hotel portfolios.

6. Engage with decision-makers who can influence brand positioning and campaign investments.

7. Investment and Development Opportunities

8. Connect with hospitality executives exploring renovations, expansions, or new property launches.

9. Identify strategic partners for joint ventures or acquisitions within the hospitality sector.

10. Recruitment and Talent Acquisition

11. Reach HR professionals or general managers looking to staff hotels with high-quality personnel.

12. Offer recruitment solutions, training programs, or staffing services directly to key decision-makers.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access top-quality verified data at competitive prices, ensuring you maximize ROI on your outreach efforts.

3. Seamless Integration

4. Integrate verified contact data into your CRM or marketing automation platforms via APIs or downloadable formats for effortless data management.

5. Data Accuracy with AI Validation

6. Trust in 99% accuracy for confident targeting, optimized conversions, and enhanced relationship-building in the hospitality sector.

7. Customizable and Scalable Solutions

8. Tailor datasets to focus on specific regions, hospitality segments, or job functions, adapting as your business goals evolve.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enrich existing CRM records with verified hospitality contact data to sharpen targeting and personalization.

3. Lead Generation API

4. Automate lead generation, streamlining your outreach and enabling efficient scalin...

Success.ai’s Event Data for Event Planning & Hospitality Professionals Worldwide delivers a comprehensive dataset tailored to help businesses connect with professionals in the global event planning and hospitality industries. Covering event organizers, venue managers, hospitality executives, and event service providers, this dataset provides verified contact details, business insights, and professional histories.

With access to over 700 million verified global profiles, Success.ai ensures your outreach, marketing, and partnership strategies are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution enables you to thrive in the competitive event and hospitality sectors.

Why Choose Success.ai’s Event Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of event planners, hospitality managers, and venue executives.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and improving communication effectiveness.

2. Comprehensive Global Coverage

   • Includes profiles of professionals from major event and hospitality hubs such as North America, Europe, Asia-Pacific, and the Middle East.
   • Gain insights into regional trends in event management, venue selection, and hospitality services.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, event service offerings, and market dynamics.
   • Stay aligned with evolving industry needs and emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage for business initiatives.

Data Highlights

• 700M+ Verified Global Profiles: Connect with event planners, hospitality professionals, and service providers worldwide.
• Leadership and Professional Histories: Access detailed career insights, certifications, and areas of expertise for industry professionals.
• Business Insights: Gain visibility into venue details, event service providers, and organizational structures.
• Regional and Industry Trends: Understand global trends in event planning, hospitality services, and customer engagement.

Key Features of the Dataset:

1. Professional Profiles in Event Planning and Hospitality

   • Identify and connect with event organizers, hospitality managers, and venue directors responsible for event coordination and guest experiences.
   • Target professionals managing large-scale events, corporate gatherings, weddings, and hospitality services.

2. Advanced Filters for Precision Targeting

   • Filter professionals by industry focus (corporate events, luxury hospitality, trade shows), geographic location, or job function.
   • Tailor campaigns to align with specific event categories, audience needs, and service offerings.

3. Event and Venue Data Insights

   • Access data on event trends, venue capacities, and service specializations to refine your strategies.
   • Leverage these insights to align offerings with industry demand and client expectations.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing and Lead Generation

   • Promote event management tools, hospitality software, or venue services to professionals in the industry.
   • Use verified contact data to design targeted campaigns for corporate events, trade shows, or private gatherings.

2. Partnership Development and Collaboration

   • Build relationships with event organizers, venue managers, and service providers seeking strategic partnerships.
   • Foster alliances that expand service offerings, enhance guest experiences, or streamline event operations.

3. Market Research and Competitive Analysis

   • Analyze trends in event planning, customer preferences, and hospitality services to refine your business strategies.
   • Benchmark against competitors to identify growth opportunities, market gaps, and high-demand event categories.

4. Recruitment and Talent Solutions

   • Target HR professionals and hiring managers recruiting for roles in event planning, hospitality management, or customer service.
   • Provide workforce optimization tools or training platforms tailored to the event and hospitality sectors.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality event and hospitality data at competitive prices, ensuring strong ROI for your outreach, marketing, and strategic initiatives.

2. Seamless Integration

   • Integrate verified event data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, simplifying workflows and enhancing productivity.

3. Data Accuracy with AI Val...

A Data Article on Unveiling Key Drivers for Social Robot Adoption in the Hospitality Sector: Two-Phase Confirmatory Factor Analysis and Structural Equation Modeling Approach • The data collected helps to identify the intention to adopt social robots. The present study will help the practitioners work on major problem factors for improving the quality of partnering relationships between key participants in their present and future hospitality sectors. By removing or minimizing these problem factors, the practitioners will be contributing considerably towards effective hotel sectors. • A preliminary questionnaire was sent to experts (ten prominent academic scholars and seven working professionals in the robotics and hotel industries) to evaluate its sufficiency and completeness. The academic experts were selected based on their work in robotics and the hospitality industry. • In addition, the specialists' current employment in the hospitality business was a criterion for their selection. Five academic scholars and four industry workers volunteered to respond to the survey. • The target respondents were employees in five-star hotels who were knowledgeable about various technology adoptions. The target respondents were employees of five-star hotels with expertise in various technology adoptions. This dataset serves as a valuable resource for the hospitality sector to gain insights into technology readiness and adoption within the industry. It is intended solely for academic, teaching, and training purposes.

Overview

This dataset provides comprehensive information on customer visits to restaurants, including demographic details, visit-specific metrics, and customer satisfaction ratings. It is designed to facilitate predictive modeling and analytics in the hospitality industry, focusing on factors that drive customer satisfaction.

Features

Demographic Information

• CustomerID: Unique identifier for each customer.
• Age: Age of the customer.
• Gender: Gender of the customer (Male/Female).
• Income: Annual income of the customer in USD.

Visit-specific Variables

• VisitFrequency: How often the customer visits the restaurant (Daily, Weekly, Monthly, Rarely).
• AverageSpend: Average amount spent by the customer per visit in USD.
• PreferredCuisine: The type of cuisine preferred by the customer (Italian, Chinese, Indian, Mexican, American).
• TimeOfVisit: The time of day the customer usually visits (Breakfast, Lunch, Dinner).
• GroupSize: Number of people in the customer's group during the visit.
• DiningOccasion: The occasion for dining (Casual, Business, Celebration).
• MealType: Type of meal (Dine-in, Takeaway).
• OnlineReservation: Whether the customer made an online reservation (0: No, 1: Yes).
• DeliveryOrder: Whether the customer ordered delivery (0: No, 1: Yes).
• LoyaltyProgramMember: Whether the customer is a member of the restaurant's loyalty program (0: No, 1: Yes).
• WaitTime: Average wait time for the customer in minutes.

Satisfaction Ratings

• ServiceRating: Customer's rating of the service (1 to 5).
• FoodRating: Customer's rating of the food (1 to 5).
• AmbianceRating: Customer's rating of the restaurant ambiance (1 to 5).

Target Variable

• HighSatisfaction: Binary variable indicating whether the customer is highly satisfied (1) or not (0).

Potential Applications

• Predictive modeling of customer satisfaction.
• Analyzing factors that drive customer loyalty and satisfaction.
• Identifying key areas for improvement in service, food, and ambiance.
• Optimizing marketing strategies to attract and retain satisfied customers.

Usage

This dataset is ideal for data scientists and hospitality analysts looking to explore and model customer satisfaction in the restaurant industry. It can be used for machine learning projects, customer segmentation, and strategic planning.

Dataset Usage and Attribution Notice

This dataset, shared by Rabie El Kharoua, is original and has never been shared before. It is made available under the CC BY 4.0 license, allowing anyone to use the dataset in any form as long as proper citation is given to the author. A DOI is provided for proper referencing. Please note that duplication of this work within Kaggle is not permitted.

Exclusive Synthetic Dataset

This dataset is synthetic and was generated for educational purposes, making it ideal for data science and machine learning projects. It is an original dataset, owned by Mr. Rabie El Kharoua, and has not been previously shared. You are free to use it under the license outlined on the data card. The dataset is offered without any guarantees. Details about the data provider will be shared soon.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

This dataset is about book subjects, has 3 rows and is filtered where the books is Accounting for the hospitality industry. It features 10 columns including book subject, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

Dataset Overview The "Restaurants in Germany" dataset contains 2000 entries detailing various attributes of restaurants located across Germany. This dataset is structured to provide insights into the restaurant industry, including location, cuisine type, pricing, and customer ratings. It is ideal for exploratory data analysis, machine learning applications, and business intelligence in the food and hospitality sector. Data Science Applications This dataset can be applied in several data science scenarios:

Customer Segmentation: Analyzing customer preferences based on restaurant ratings and cuisine types.
Recommendation Systems: Building models to recommend restaurants based on user preferences.
Market Analysis: Identifying trends in restaurant pricing and popularity across different regions.
Predictive Analytics: Forecasting customer satisfaction or sales based on historical data.
Geospatial Analysis: Mapping restaurant density and identifying underserved areas.

Column Descriptors

The dataset includes the following columns:

Restaurant Name: The name of the restaurant.
Cuisine Type: The type of cuisine offered (e.g., Italian, German, Asian).
Location: The city or region where the restaurant is located.
Price Range: The average cost per meal (e.g., low, medium, high).
Customer Rating: Average rating provided by customers (scale of 1 to 5).
Number of Reviews: Total number of reviews received by the restaurant.
Opening Hours: Typical opening hours (e.g., 10:00 AM - 10:00 PM).
etc

Ethically Mined Data

This dataset has been ethically sourced and is intended strictly for educational purposes. Users are encouraged to respect data privacy laws and avoid using this dataset for commercial or unethical purposes.

Acknowledgment

extend gratitude to the platforms and sources that contributed to compiling this dataset. Their efforts make it possible to advance education and research in data science.

The TripAdvisor Vietnam Hotel Reviews Dataset is a comprehensive collection of user-generated reviews from the popular online travel platform TripAdvisor. This dataset offers valuable insights into the experiences, opinions, and ratings provided by individuals who have stayed at various hotels across Vietnam.

The dataset encompasses many hotels in different cities and regions of Vietnam, including popular tourist destinations such as Hanoi, Ho Chi Minh City, Da Nang, Nha Trang, and more. The reviews cover a diverse spectrum of accommodation types, ranging from budget guesthouses to luxurious resorts, providing a comprehensive representation of the Vietnamese hospitality industry.

Each review entry in the dataset includes a rich set of information, offering researchers, developers, and data analysts an in-depth understanding of hotel performance and customer satisfaction. Key attributes of the dataset include:

Review Text: The actual text of the review left by the user, which contains detailed descriptions, opinions, and feedback about their hotel experience.

Rating: The overall rating provided by the reviewer, typically ranging from 1 to 5 stars, reflects their satisfaction level with the hotel.

Date: The review was posted, enabling temporal analysis and tracking changes over time.

Location: The hotel's geographic location allows researchers to analyze regional variations in hotel performance and customer preferences.

The TripAdvisor Vietnam Hotel Reviews Dataset is valuable for various applications, including sentiment analysis, opinion mining, natural language processing, customer behavior analysis, recommender systems, and more. Researchers can leverage this dataset to gain deep insights into customer experiences, identify patterns, trends, and sentiments, and develop data-driven strategies for the Vietnamese hotel industry.

This dataset is a comprehensive collection of research outputs and scholarly articles categorized under various disciplines related to the hospitality sector. It covers a wide array of topics including "Hospitality Leisure Sport Tourism," "Economics," "Business," "Management," "Green Sustainable Science Technology," and many others. The aim of this collection is to facilitate a multidisciplinary approach to studying the hospitality industry, providing valuable insights from environmental sciences to social sciences, and from applied psychology to computer science applications.

The dataset has been curated with the intention to support researchers, practitioners, and policymakers in the development of innovative strategies for sustainable and profitable hospitality management. It includes quantitative and qualitative data on customer satisfaction, economic impact studies, environmental management within the tourism sector, and technological advancements in hotel and tourism management. Furthermore, it encompasses social and cultural studies pertinent to hospitality, examining behavioral patterns, communication strategies, and societal impacts of tourism.

This dataset is about books. It has 1 row and is filtered where the book is Modern financial accounting in the hospitality industry. It features 7 columns including author, publication date, language, and book publisher.

Context

Airbnb has successfully disrupted the traditional hospitality industry as more and more travelers decide to use Airbnb as their primary accommodation provider. Since its beginning in 2008, Airbnb has seen an enormous growth, with the number of rentals listed on its website growing exponentially each year. Amsterdam is a city always buzzing with travelers. Good food, diverse crown and vibrant culture makes it a favorite travel destination for many. Airbnb started its Amsterdam operations a few years ago and since then, Airbnbs have been preferred places to stay for travelers.

Content

This data is an open source dataset downloaded from http://insideairbnb.com/get-the-data.html with a cut-off date of 16th April 2020. This is a summary dataset, which means a lot of non-essential columns have been trimmed to make the data easy to use and understand.

Acknowledgements

A big shout out to Airbnb for providing open source data to developers to practice and improve their analysis skills.

Inspiration

Airbnb data is a great source to understand how hotel and stay business works. This dataset has factors like pricing, reviews and availability which we all, knowingly or unknowingly, come across while booking accommodation. I loved analyzing this data and generating insights from this. Are you ready to generate yours ?

This dataset is about book subjects. It has 2 rows and is filtered where the books is Hospitality law : managing legal issues in the hospitality industry. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset provides the total number of employees in the hotels and restaurants sector in Qatar, aggregated across all establishments regardless of size. The data is disaggregated by gender, nationality, and economic activity, allowing detailed workforce profiling for the hospitality industry.