A hotel dataset that includes ratings and prices typically consists of information about various hotels and their corresponding attributes. Below is a general description of what such a dataset might include:

1. Hotel Information: Hotel ID: A unique identifier for each hotel in the dataset. Hotel Name:The name or title of the hotel. Location:The geographical location or address of the hotel, including details such as city, state, and country.

2.Rating Information: User Ratings:** Ratings provided by users or guests who have stayed at the hotel. Ratings can be on a numerical scale (e.g., 1 to 5 stars) or in another format. Average Rating:The overall average rating of the hotel based on user reviews.

3.Price Information: Room Prices:The cost of different types of rooms offered by the hotel. This may include standard rooms, suites, and other accommodation options. Price Range: The range of prices for different room types.

This type of dataset is valuable for various purposes, such as helping users find hotels that match their preferences based on ratings and prices, conducting data analysis on the hospitality industry, and training machine learning models for predicting hotel ratings or prices based on certain features. Researchers, data analysts, and businesses in the travel and hospitality sector may find such datasets useful for different analyses and decision-making processes.

About columns

1- ID : unique identifier of each booking

2- n_adults : Number of adults

3- n_children : Number of Children

4- weekend_nights : Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel

5- week_nights : Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel

6- meal_plan : Type of meal plan booked by the customer

7- car_parking_space : Does the customer require a car parking space? (0 - No, 1- Yes)

8- room_type: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.

9- lead_time: Number of days between the date of booking and the arrival date

10- year : Year of arrival date

11- month : Month of arrival date

12- date : Date of the month

13- market_segment : Market segment designation.

14- repeated_guest : Is the customer a repeated guest? (0 - No, 1- Yes)

15- previous_cancellations : Number of previous bookings that were canceled by the customer prior to the current booking

16- previous_bookings_not_canceled : Number of previous bookings not canceled by the customer prior to the current booking

17- avg_room_price : Average price per day of the reservation; prices of the rooms are dynamic. (in euros)

18- special_requests : Total number of special requests made by the customer (e.g. high floor, view from the room, etc)

19- status : Flag indicating if the booking was canceled or not.

Description :

This dataset contains a snapshot of 2,300+ hotel listings from Gujarat, India, captured on a single day. It's designed for travel recommendation systems, pricing analysis, and exploratory data science projects.

Each Record Provides :

• Hotel Name
• Location (City, Nearby Places)
• Rating & Review Text
• Discounted and Actual Prices
• Facilities like Spa, Pool, Restaurant, etc.
• Destination Name

Ideal for :

• Hotel recommendation systems
• Tourism and pricing analytics
• Facility-based hotel clustering
• Feature engineering and EDA
• Machine learning model training

Context

Tourism and travel holds more than 10% of the GDP worldwide, and is trending towards capturing higher stakes of the global pie. At the same time, it's an industry that generates huge volume of data and getting advantage of it could help businesses to stand out from the crowd.

Content

The dataset provides reservations data for two consecutive seasons (2021 - 2023) of a luxury hotel.

Source

ChatGPT 3.5 (OpenAI) is the main creator of the dataset. Minor adjustments were performed by myself to ensure that the dataset contains the desired fields and values.

Inspiration

• How effectively is the hotel performing across key metrics? • How are bookings distributed across different channels (e.g., Booking Platform, Phone, Walk-in, and Website)? • What is the current occupancy rate and how does it compare to the same period last year? • What are the demographics of the current guests (e.g., nationality)? • What is the average daily rate (ADR) per room?

These are examples of interesting questions that could be answered by analyzing this dataset.

If you are interested, please have a look at the Tableau dashboard that I have created to help answer the above questions. Tableau dashboard: https://public.tableau.com/app/profile/dimitris.angelides/viz/HotelExecutiveDashboards/HotelExecutiveSummaryReport?publish=yes

Data on the average achieved hotel room rate by hotel category in Hong Kong in the past five years

Hong Kong Average Achieved Hotel Room Rate: All Hotels data was reported at 1,567.000 HKD in Oct 2018. This records an increase from the previous number of 1,305.000 HKD for Sep 2018. Hong Kong Average Achieved Hotel Room Rate: All Hotels data is updated monthly, averaging 1,130.500 HKD from Jul 1998 (Median) to Oct 2018, with 244 observations. The data reached an all-time high of 1,678.000 HKD in Oct 2012 and a record low of 552.000 HKD in Feb 1999. Hong Kong Average Achieved Hotel Room Rate: All Hotels data remains active status in CEIC and is reported by Hong Kong Tourism Board. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Q023: Hotel Statistics: Average Achieved Hotel Room Rate.

Dataset for collection of booking information of hotels guests 🏬

About columns

• ID : unique identifier of each booking
• n_adults : Number of adults
• n_children : Number of Children
• weekend_nights : Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
• week_nights : Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
• meal_plan : Type of meal plan booked by the customer:
• car_parking_space : Does the customer require a car parking space? (0 - No, 1- Yes)
• room_type : Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
• lead_time : Number of days between the date of booking and the arrival date
• year : Year of arrival date
• month : Month of arrival date
• date : Date of the month
• market_segment : Market segment designation.
• repeated_guest : Is the customer a repeated guest? (0 - No, 1- Yes)
• previous_cancellations : Number of previous bookings that were canceled by the customer prior to the current booking
• previous_bookings_not_canceled : Number of previous bookings not canceled by the customer prior to the current booking
• avg_room_price : Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
• special_requests : Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
• status : Flag indicating if the booking was canceled or not.

IHIS: Average Room Rates per Hotel: Lucknow data was reported at 4,442.000 INR in 2017. This records a decrease from the previous number of 4,847.000 INR for 2016. IHIS: Average Room Rates per Hotel: Lucknow data is updated yearly, averaging 2,300.500 INR from Mar 1999 (Median) to 2017, with 18 observations. The data reached an all-time high of 5,401.000 INR in 2015 and a record low of 1,129.000 INR in 2003. IHIS: Average Room Rates per Hotel: Lucknow data remains active status in CEIC and is reported by The Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHD004: Indian Hotel Industry Survey: Average Room Rates per Hotel: by Cities.

Monthly and annual hotel occupancy rate (%) in the past five years (provided by Hong Kong Tourism Board. For more information, please visit https://partnernet.hktb.com)

Data includes occupancy rates, average daily rates, and revenue per available room.

The Hotel Area KPIs dataset provides comprehensive insights into hotel performance metrics across global markets.

Sourced directly from hotel reservation systems, this dataset offers a real-time view of key performance indicators such as occupancy rates, average daily rates (ADR), revenue per available room (RevPAR), and booking patterns.

With weekly updates and both historical and forward-looking data, it enables hoteliers, investors, and analysts to track market trends, benchmark performance, and make data-driven decisions.

This dataset is invaluable for understanding seasonal variations, forecasting demand, and optimizing pricing strategies in the dynamic hospitality industry.

China Hotel Room Occupancy Rate: Total data was reported at 50.690 % in 2023. This records an increase from the previous number of 38.350 % for 2022. China Hotel Room Occupancy Rate: Total data is updated yearly, averaging 56.180 % from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 61.030 % in 2006 and a record low of 38.350 % in 2022. China Hotel Room Occupancy Rate: Total data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under Global Database’s China – Table CN.QHA: Star-Rated Hotel: Room Occupancy Rate.

Hong Kong Average Achieved Hotel Room Rate: High Tariff A data was reported at 2,428.000 HKD in Oct 2018. This records an increase from the previous number of 2,059.000 HKD for Sep 2018. Hong Kong Average Achieved Hotel Room Rate: High Tariff A data is updated monthly, averaging 1,929.500 HKD from Jul 1998 (Median) to Oct 2018, with 244 observations. The data reached an all-time high of 2,721.000 HKD in Oct 2012 and a record low of 969.000 HKD in Aug 2003. Hong Kong Average Achieved Hotel Room Rate: High Tariff A data remains active status in CEIC and is reported by Hong Kong Tourism Board. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Q023: Hotel Statistics: Average Achieved Hotel Room Rate.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_8e62605f0c1c948702b6ea0fe45242d3/view

his dataset provides comprehensive insights into the operational and revenue performance of a hotel throughout the year 2024. It includes detailed records of daily operations, revenue figures, guest demographics, booking sources, economic indicators, and more. Key features encompass:

Date: The date of the recorded data. Month: Numeric representation of the month. Day of the Week: Numeric representation of the day in a week. Season: Categorical representation of the season (e.g., Winter, Spring, Summer, Fall). Public Holiday: Binary indicator (0 or 1) denoting whether it's a public holiday. Previous Month Revenue: Revenue generated in the previous month. Year-over-Year Revenue: Revenue compared to the same month the previous year. Monthly Trend: Trend in revenue or occupancy for the month. Occupancy Rate: Percentage of rooms occupied. Average Daily Rate (ADR): Average rate charged per occupied room. Revenue per Available Room (RevPAR): Revenue generated per available room. Booking Lead Time: Average lead time between booking and stay. Booking Cancellations: Percentage of bookings cancelled. Booking Source: Source of the booking (e.g., Direct, OTA). Guest Type: Type of guest (e.g., Leisure, Business). Repeat Guests: Percentage of guests who are repeat visitors. Nationality: Nationality of guests. Group Bookings: Binary indicator denoting group bookings. Discounts and Promotions: Use of discounts or promotions. Room Rate: Average rate charged for rooms. Local Events: Presence of local events influencing occupancy. Hotel Events: Events hosted by the hotel affecting operations. Competitor Rates: Rates offered by competitors. Weather Conditions: Local weather conditions influencing guest behavior. Economic Indicators: Economic factors influencing hotel performance. Staff Levels: Staffing levels affecting service quality. Guest Satisfaction: Guest satisfaction ratings. Maintenance Issues: Issues related to maintenance affecting operations. Marketing Spend: Expenditure on marketing activities. Online Reviews: Ratings and reviews provided online. Social Media Engagement: Engagement metrics on social media platforms. Seasonal Adjustments: Adjustments made for seasonal variations. Trend Adjustments: Adjustments made for trending factors. Room Revenue: Total revenue from room bookings. Food and Beverage Revenue: Revenue from food and beverage services. Other Services Revenue: Revenue from other hotel services. Total Revenue for the Month: Overall revenue generated for the month.

Sri Lanka Hotel Room Occupancy Rate: 5 Stars data was reported at 74.640 % in 2017. This records a decrease from the previous number of 74.810 % for 2016. Sri Lanka Hotel Room Occupancy Rate: 5 Stars data is updated yearly, averaging 74.800 % from Dec 2011 (Median) to 2017, with 7 observations. The data reached an all-time high of 78.700 % in 2011 and a record low of 71.500 % in 2012. Sri Lanka Hotel Room Occupancy Rate: 5 Stars data remains active status in CEIC and is reported by Sri Lanka Tourism Development Authority. The data is categorized under Global Database’s Sri Lanka – Table LK.Q009: Hotel Room Occupancy Rate.

Historic and forward-looking hotel performance metrics sourced directly from global reservation systems.

The Hotel Area KPIs Dataset provides comprehensive visibility into hotel market performance across global destinations. Sourced from leading hotel reservation systems, it delivers verified occupancy rates, average daily rates (ADR), revenue per available room (RevPAR), and booking trends — offering both historical and future-looking insights at the market or submarket level.

With weekly updates and standardized data across regions, this dataset empowers analysts, investors, and developers to evaluate market strength, forecast demand, and benchmark asset performance with confidence. Its accuracy and depth make it a critical input for feasibility studies, portfolio analysis, and economic modeling in the hospitality sector.

Key Highlights: Verified Source Data: Derived directly from hotel reservation systems for unmatched reliability.

Comprehensive Metrics: Includes occupancy, ADR, RevPAR, booking pace, and forecasted demand.

Global Market Coverage: Standardized data across regions, cities, and competitive sets.

Temporal Depth: Historical trends and future on-the-books performance updated weekly.

Flexible Access: Delivered via API or downloadable datasets for seamless integration into analytical workflows.

Ideal For: Hospitality & Real Estate Investors: Benchmark hotel markets and model income potential.

Developers & Feasibility Analysts: Support site selection and market-entry decisions with demand-based KPIs.

Financial Institutions & Advisors: Underwrite hotel and mixed-use projects with forward-looking performance indicators.

Tourism Economists & Research Firms: Track market recovery, seasonality, and macro performance trends.

Use It To: Evaluate market performance and identify under- or over-performing destinations.

Forecast revenue and demand based on forward-booking data.

Benchmark assets within peer groups or competitive sets.

Support development feasibility and capital allocation decisions.

IHIS: Average Room Rates per Hotel: Hyderabad data was reported at 3,469.000 INR in 2017. This records a decrease from the previous number of 4,392.000 INR for 2016. IHIS: Average Room Rates per Hotel: Hyderabad data is updated yearly, averaging 3,469.000 INR from Mar 1999 (Median) to 2017, with 19 observations. The data reached an all-time high of 5,643.000 INR in 2008 and a record low of 1,131.000 INR in 2002. IHIS: Average Room Rates per Hotel: Hyderabad data remains active status in CEIC and is reported by The Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHD004: Indian Hotel Industry Survey: Average Room Rates per Hotel: by Cities.

Dataset is about easily finding an ideal hotel and comparing prices from different websites. Hence deciding the best hotel search comparing accommodation prices. Also refining search results, simply filter by price, distance (e.g. from the beach), star category, facilities, and more. It shows the average rating and extensive reviews from other booking sites, e.g. Hotels.com, Expedia, Agoda, leading hotels, etc. The dataset includes hotel budgets from luxury suites to the heavenly paradise resorts. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8355503%2F892a4d89a1795973d64403c1fc9beace%2FHotels%20rev.PNG?generation=1698592504477953&alt=media" alt=""> It includes a large variety of rooms and locations across different popular cities and holiday destinations in the USA.

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.