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.

Booking.com Accommodation Review Dataset

This repository contains the training set of the user-generated review dataset of Booking.com reviews. The training set contains about 1.6M reviews from 40k accommodations around the world. All reviews were written by guests who stayed at the accommodation. The dataset consists of English reviews published in 2023. All reviews have passed a moderation process ensuring they are genuine and do not violate the platform guidelines. In order… See the full description on the dataset page: https://huggingface.co/datasets/Booking-com/accommodation-reviews.

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.

Here are a few use cases for this project:

1. Hotel Booking Websites: The "Hotel Image Tagging" model can be used by hotel booking websites to automatically tag and categorize room images based on their features (e.g. AC, TV, bed types, etc.), thus improving image search and filtering options for users while they search for suitable accommodations.

2. Virtual Hotel Tours: Hotel businesses can use the model to create interactive virtual tours of their rooms and facilities. The computer vision model can identify and highlight the key amenities in each space (e.g., sofa, chair, and TV), enhancing the virtual tour experience for potential guests.

3. Hotel Inventory Management: Hotels could leverage the "Hotel Image Tagging" model to monitor their room amenities for maintenance purposes. By processing images regularly, the model can help identify worn or damaged items (chairs, sofas, etc.) that need replacement, improving overall customer satisfaction.

4. Smart Hotel Recommendation System: Travel agencies or hotel booking websites can implement an AI-driven recommendation system that uses the computer vision model to determine which amenities are favored by a particular user. By analyzing the history of hotel images viewed or booked by the user, the system can make personalized hotel suggestions that cater to their preferences.

5. Hotel Marketing and Advertising: The "Hotel Image Tagging" model can be an asset in producing targeted marketing campaigns or sales promotions. It can analyze the popularity of specific amenities within the hotel's photos and recommend which features to highlight in promotional materials to attract more guests.

Unlock valuable insights with our comprehensive TripAdvisor Dataset, designed for businesses, analysts, and researchers to track customer reviews, ratings, and travel trends. This dataset provides structured and reliable data from TripAdvisor to enhance market research, competitive analysis, and customer satisfaction strategies.

Dataset Features

Business Listings: Access detailed information on hotels, restaurants, attractions, and other businesses, including names, locations, categories, and contact details. Customer Reviews & Ratings: Extract user-generated reviews, star ratings, review dates, and sentiment analysis to understand customer experiences and preferences. Pricing & Booking Data: Track pricing trends, availability, and booking options for hotels, flights, and travel services. Location & Geographical Insights: Analyze travel trends by region, city, or country to identify popular destinations and emerging markets.

Customizable Subsets for Specific Needs Our TripAdvisor Dataset is fully customizable, allowing you to filter data based on location, business type, review sentiment, or specific keywords. Whether you need broad coverage for industry analysis or focused data for customer insights, we tailor the dataset to your needs.

Popular Use Cases

Customer Satisfaction & Brand Monitoring: Track customer feedback, analyze sentiment, and improve service offerings based on real user reviews. Market Research & Competitive Analysis: Compare business performance, monitor competitor reviews, and identify industry trends. Travel & Hospitality Insights: Analyze travel patterns, popular destinations, and seasonal trends to optimize marketing strategies. AI & Machine Learning Applications: Use structured review data to train AI models for sentiment analysis, recommendation engines, and predictive analytics. Pricing Strategy & Revenue Optimization: Monitor pricing trends and customer demand to optimize pricing strategies for hotels, restaurants, and travel services.

Whether you're analyzing customer sentiment, tracking travel trends, or optimizing business strategies, our TripAdvisor Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Since the original dataset was a real hotel dataset, of real customers, all the elements pertaining hotel or costumer identification were deleted.

All the values of the columns: 'name', 'email', 'phone number' and 'credit_card' have been artificially created using a python and filled into the dataset.

Column Descriptions for Hotel Booking Dataset

This dataset contains information on hotel bookings, encompassing details about guests, their reservations, and hotel attributes. It's a valuable resource for analysing and predicting trends in the hospitality industry.

• hotel: The type of hotel, either "City Hotel" or "Resort Hotel."

• is_canceled: Binary value indicating whether the booking was cancelled (1) or not (0).

• lead_time: Number of days between booking and arrival.

• arrival_date_year: Year of arrival date.

• arrival_date_month: Month of arrival date.

• arrival_date_week_number: Week number of arrival date.

• arrival_date_day_of_month: Day of the month of arrival date.

• stays_in_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stays.

• stays_in_week_nights: Number of weekday nights (Monday to Friday) the guest stays.

• adults: Number of adults.

• children: Number of children.

• babies: Number of babies.

• meal: Type of meal booked.

• country: Country of origin.

• market_segment: Market segment designation.

• distribution_channel: Booking distribution channel.

• is_repeated_guest: Binary value indicating whether the guest is a repeated guest (1) or not (0).

• previous_cancellations: Number of previous booking cancellations.

• previous_bookings_not_canceled: Number of previous bookings not cancelled.

• reserved_room_type: Code of room type reserved.

• assigned_room_type: Code of room type assigned at check-in.

• booking_changes: Number of changes/amendments made to the booking.

• deposit_type: Type of deposit made.

• agent: ID of the travel agency.

• company: ID of the company.

• days_in_waiting_list: Number of days in the waiting list before booking.

• customer_type: Type of booking.

• adr: Average daily rate.

• required_car_parking_spaces: Number of car parking spaces required.

• total_of_special_requests: Number of special requests made.

• reservation_status: Reservation last status.

• reservation_status_date: Date of the last status.

• name: Guest's name. (Not Real)

• email: Guest's email address.(Not Real)

• phone-number: Guest's phone number. (Not Real)

• credit_card: Guest's credit card details. (Not Real)

Explore this dataset to gain insights into booking trends, cancellations, and guest behaviour in the hotel industry.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Multiple signatures : on designers, authors, readers and users. It features 4 columns: authors, books, and publication dates.

This dataset is about books. It has 1 row and is filtered where the book is Scaling conversations : how leaders access the full potential of people. It features 4 columns: author, book publisher, and BNB id.

Project Goal: The aim of this project is to gain insights into the factors that affect hotel bookings and cancellations, and to identify patterns in customer behavior that could help the hotel optimize its operations and improve customer satisfaction.

1.What are the most popular room types and meal plans among customers? 2.How does the lead time (number of days between booking and arrival) affect the likelihood of cancellation? 3.What are the most common reasons for cancellation, and how can they be addressed to reduce the cancellation rate? 4.Are there any trends in booking patterns over time (e.g., seasonal variations, changes in market segments)? 5.What is the average revenue per reservation for a hotel? 6.What is the average revenue loss due to booking cancellations? 7.Which month do we lose the most revenue compared to the revenue we gain? 8.Analyze the average lead time of booking cancellations and non-cancellations in the month of December ? 9.How do the number of adults, children, and special requests influence the type of room and meal plan selected by customers? 10.What are the most popular days of the week and times of year for hotel bookings? 11.​How do prices vary depending on the time of year, day of the week, and lead time? 12.What is the impact of being a repeated guest on booking behavior and cancellation rates? 13.How does the availability of car parking space affect the booking behavior of customers?

This dataset is about books, has 1 rows and is filtered where the book is The management system : systems are for people. It features 4 columns: book, author, book publisher, and BNB id. The preview is ordered by publication date (descending).

This dataset is about book series, has 1 rows and is filtered where the books is How to manage the IT helpdesk : a guide for user support and call centre managers. It features 10 columns including book series, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

This dataset is about book subjects. It has 3 rows and is filtered where the books is Forget school : why young people are succeeding on their own terms and what schools can do to avoid being left behind. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Acknowledgements

The data was scraped from La Veranda Hotel on Booking.com. All data in the file is publicly available to everyone already. Please be noted that data is originally owned by Booking.com.

Data Context

This dataset contains 1,500 customer reviews for an hotel in Larnaca-Cyprus. The hotel opened exactly one year before the data was scraped, and these reviews provide valuable insight into the experiences of guests who stayed at the property during its first year of operation.

Data Content

The columns in the dataset are:

1. Title: A brief summary of the review written by the guest.
2. Positive Review: A detailed account of what the guest liked about their stay at the hotel.
3. Negative Review: A detailed account of what the guest did not like about their stay at the hotel.
4. Score: A rating out of 10 given by the guest to reflect their overall experience.
5. Guest Name: The name of the guest who wrote the review.
6. Guest Country: The country of origin of the guest who wrote the review.
7. Room Type: The type of room that the guest stayed in during their visit.
8. Number of Nights: The number of nights the guest stayed at the hotel.
9. Visit Date: The date the guest stayed at the hotel.
10. Group Type: Whether the guest was traveling alone or with a group.
11. Property Response: A response from the hotel to the guest's review.

Please note that: - Guest Name could be 'Anonymous'

Inspiration

This data can be useful for a variety of purposes, including understanding customer satisfaction levels, identifying areas for improvement, and analyzing guest demographics and behavior.

For instance, The Score and Positive Review/Negative Review columns can be used to gauge overall satisfaction and pinpoint specific areas of improvement. The Guest Country and Number of Nights columns can be used to analyze guest demographics and behavior, such as the nationalities and length of stays of guests at the hotel.

This data is an excellent resource for hotel managers and researchers looking to gain a deeper understanding of customer experiences at hotels.

The dataset originates from the book "Practical Statistics for Data Scientists" by Peter Bruce, Andrew Bruce, and Peter Gedeck.

Context:

A company selling a high-value service wants to determine which of two web presentations is more effective at selling. Due to the high value and infrequent nature of the sales, as well as the lengthy sales cycle, it would take too long to accumulate enough sales data to identify the superior presentation. Therefore, the company uses a proxy variable to measure effectiveness.

A proxy variable stands in for the true variable of interest, which may be unavailable, too costly, or too time-consuming to measure directly. In this case, the proxy variable is the amount of time users spend on a detailed interior page that describes the service.

Content:

The dataset includes a total of 36 sessions across the two web presentations: 21 sessions for page A and 15 sessions for page B. The goal is to determine if users spend more time on page B compared to page A. If users spend more time on page B, it would suggest that page B is more effective at engaging potential customers, and therefore, does a better selling job.

The time is expressed in hundredths of seconds. For example, a value of 0.1 indicates 10 seconds, and a value of 2.53 indicates 253 seconds.

These datasets were collected in late 2017 from goodreads.com, where we only scraped users' public shelves, i.e. everyone can see it on web without login. User IDs and review IDs are anonymized. We collected these datasets for academic use only. Please do not redistribute them or use for commercial purposes.

We collected three groups of datasets: (1) meta-data of the books, (2) user-book interactions (users' public shelves) and (3) users' detailed book reviews. These datasets can be merged together by joining on book/user/review ids.

This is the subset under the graphics and comics genre, with 89,411 books, 7,347,630 interactions, 542,338 detailed reviews

see more details here: https://mengtingwan.github.io/data/goodreads.html#datasets

The high rate of cancellations and no-shows in online hotel reservations has become a challenge for hotels as it impacts their revenue and occupancy rates. While customers benefit from the flexibility of free or low-cost cancellations, hotels have to deal with the revenue-diminishing effect of empty rooms. Hence, there is a need to explore strategies that can help hotels reduce cancellations and no-shows while maintaining customer satisfaction and loyalty.

This dataset is about book subjects, has 2 rows. and is filtered where the books includes Children are people : the librarian in the community. 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).

This dataset is about book subjects, has 2 rows. and is filtered where the books is 1001 more ridiculous ways to die : a comprehensive collection of humorous true stories about the most ridiculous ways people have met their maker. 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).

This dataset is about book subjects, has 2 rows. and is filtered where the books is The management system : systems are for people. 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).

This dataset is about book subjects. It has 5 rows and is filtered where the books is How Do You Kill 11 Million People? : Why the Truth Matters More Than You Think. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.