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.

Average achieved hotel room rate in Hong Kong in the past five years

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

This dataset can be used for the following applications and more:

** * Analyzing trends** Just as an example, you can see estimate how room occupancy must have been affected by the Covid 19 pandemic.

*** Sentiment Analysis / Opinion Mining** Using NLP techniques one can find out what the average user’s sentiment is towards each of the featured hotels in this dataset.

*** Topic / Aspect Extraction** Using categorization techniques one can quickly figure out how each of the hotels featured in this dataset fairs on attributes such as room quality, staff, food, check-in process, etc.

***Competitor Analysis** If you would like to find out what customers think about your competitors, a tailored dataset like the one featured in this blog post can enable you to do so with simple data analysis or visualization techniques.

Statistical information on the monthly, quarterly, annual number of places or numbers occupied by accommodation providers by administrative territory and types of accommodation has been provided. Used classifications: classification of administrative units and residential areas of the Republic of Lithuania (LR AVGVK 2018); Definitions: Accommodation is a local activity unit providing paid short-term accommodation to a tourist. “Tourist” means a natural person who travels to an area outside his normal environment, stays there for at least one night, but not more than 12 months, and has no purpose of studying or doing paid work there. “Night” means any night a tourist actually spends (sleeping or staying) or is registered (his physical presence is not necessary) in an accommodation establishment. Number (room) shall mean a specially equipped room or group of premises in a building intended for accommodation, offered by the accommodation provider to persons to rent as an indivisible whole. “Number/room occupancy” means the ratio between the number of occupied numbers/rooms and the number of rooms/rooms prepared for accommodation, expressed as a percentage. Number of places shall mean the number of beds determined in rooms or separate premises according to the number of persons to whom accommodation may be provided (double bed is two places). Occupancy is the ratio between the number of nights and the number of places ready for accommodation, expressed as a percentage. Statistical observation unit - Accommodation institution (local activity unit). The survey population shall comprise: legal persons providing accommodation services, irrespective of the main economic activity according to the following groups of Chapter I of Rev. 2 of the ECEC: 55.1 – activities of hotels and similar temporary establishments, 55.2 – activities of holidaymakers and other short-term accommodation, 55.3 – activities of recreational vehicles, trailer grounds and camping sites (hereafter: Rev. 2 I section of the ECEC, groups 55.1, 55.2, 55.3). natural persons working on a business certificate (010, 043). Geographical coverage - Statistical information shall be prepared at national, regional, county and municipal level. Time coverage - from 2012

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.

This table presents an overview of of the capacity (type of accommodation, rooms, beds) in the Netherlands in all hotels, motels, boarding houses, apartments with hotel services, youth accommodation and bed & breakfasts with at least 5 sleeping places. The figures can be broken down by star rating. Figures are available for The Netherlands as a whole, and for the city of Amsterdam.

The breakdown by star rating is based on the opinion of the accommodation itself. The star rating does not have to be officially registered. The breakdown contains all types of accommodation mentioned above, not just hotels. The '5 stars' category contains 5 star hotels, but also for instance 5 star bed&breakfasts.

Break in series: Figures on guests and overnight stays per star rating for the years until 2015, that were published before, were based on offical registrations of the number of stars by the 'Bedrijfschap Horeca en Catering'. This official registration does no longer exist. Therefore, Statistics Netherlands started asking accommodations about their number of stars in its annual survey. For this reason, the figures in this table are not directly comparable with figures published about the years until 2015.

Data available from: 2017

Status of the figures: The figures for 2025 are provisional and all other figures are final.

Changes as of 11 November 2025: The provisional figures for September 2025 have been added.

When will new figures be published? Figures of a new month become available within three months after the end of that month, these are provisional figures.

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.

Colombia Hotel Rates: Real Index data was reported at 11.631 2005=100 in May 2020. This records an increase from the previous number of 10.021 2005=100 for Apr 2020. Colombia Hotel Rates: Real Index data is updated monthly, averaging 127.868 2005=100 from Jul 2004 (Median) to May 2020, with 191 observations. The data reached an all-time high of 221.798 2005=100 in Dec 2019 and a record low of 10.021 2005=100 in Apr 2020. Colombia Hotel Rates: Real Index data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.Q002: Hotel Rates and Average Room Rate Index: 2005=100.

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)

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

Hotel Bookings Analysis

Analyzing Hotel Bookings and Cancellations

By Mesum Raza Hemani [source]

About this dataset

The Hotel Bookings dataset is a comprehensive collection of information regarding hotel bookings, cancellations, and guests' details. This dataset provides insights into various aspects such as the type of hotel, the number of adults, children and babies per booking, the length of stay in both weekend nights (Saturday or Sunday) and weekdays (Monday to Friday), the meal plan chosen by guests, their country of origin and market segment designation.

Additionally, this dataset includes significant information about reservation status and its updates. It covers whether a booking was canceled or not, the lead time between booking date and arrival date, the week number and day of arrival date. It also indicates if a guest is a repeated visitor or a new customer.

The dataset contains information related to room assignments as well. It mentions both reserved room types (the type of room initially requested) as well as assigned room types (the room actually allocated). Furthermore, it reveals any changes made to bookings along with details about previous cancellations made by guests.

Other relevant factors in this dataset are deposit type for each booking; ID numbers for travel agencies used in making reservations; days spent on waiting lists before confirmation; customer classification such as transient or group; average daily rate calculated based on lodging transactions divided by total staying nights; required car parking spaces indicated by customers; the total count of special requests made by each guest.

The provided data can facilitate analysis on several levels: studying specific hotels within different periods (including year), understanding trends across months and weeks within those years to identify preferred seasons among guests from various countries represented in terms of proportions over other nations. An examination can be conducted for differences between adult-only bookings vs family-oriented ones considering associated variables like stayed weekend/week nights for conversations around how these two groups differ when it comes to selecting their staying patterns at hotels.

This expansive dataset has great potential for an in-depth exploration into various aspects involved in hotel bookings processes while providing valuable insights for improving hotel services, optimizing operations, and understanding customer preferences

How to use the dataset

Introduction:

• Understanding the Columns:
• hotel: Type of hotel (Categorical)
• is_canceled: Whether the booking was canceled or not (Binary)
• lead_time: Number of days between booking date and arrival date (Numeric)
• arrival_date_year: The year of the arrival date (Numeric)
• arrival_date_month: The month of the arrival date (Categorical)
• arrival_date_week_number: The week number of the arrival date (Numeric)
• arrival_date_day_of_month: The day of the month of the arrival date (Numeric)
• stays_in_weekend_nights: Number of weekend nights stayed or booked to stay at the hotel (Numeric)
• stays_in_week_nights: Number of week nights stayed or booked to stay at the hotel (Numeric)

• adults, children, babies: Number of guests categorized by age groups

  • adults = Number of adults
  • children = Number of children
  • babies = Number infants

• Booking Details:

  • meal: Type(s) food option(s) included in booking package (Categorical)
  • country & market_segment & distribution_channel columns provide demographic and customer classification information.
  • is_repeated_guest column specifies whether a guest is a repeated visitor or not.
  • previous_cancellations column indicates how many previous bookings were canceled by a guest.
  • previous_bookings_not_canceled shows how many previous bookings were not canceled by a guest.

• Accommodation Details:

  • reserved_room_type column indicates which type room was originally reserved for each booking. assigned_room_type mentions which type room was finally assigned for each booking.
  • booking_changes: Number of changes made to the booking before arrival.
  • deposit_type: Type of deposit made for the booking (Categorical).
  • agent & company columns provide relevant information about the travel agency and/or company involved in making the reservation.

• Additional Information:

  • days_in_waiting_list: Number of days the booking was on a waiting list before it was confirmed or canceled.
  • customer_type provides information on types of customers (Categorical)
  • adr:...

IHIS: Average Room Rates per Hotel: Mussoorie data was reported at 5,467.000 INR in 2017. This records a decrease from the previous number of 5,670.000 INR for 2016. IHIS: Average Room Rates per Hotel: Mussoorie data is updated yearly, averaging 2,997.000 INR from Mar 1999 (Median) to 2017, with 13 observations. The data reached an all-time high of 6,078.000 INR in 2010 and a record low of 656.000 INR in 2006. IHIS: Average Room Rates per Hotel: Mussoorie 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.

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.

Shanghai: Star-Rated Hotel: Average Room Rate data was reported at 748.000 RMB in Mar 2025. This records an increase from the previous number of 701.000 RMB for Feb 2025. Shanghai: Star-Rated Hotel: Average Room Rate data is updated monthly, averaging 652.040 RMB from Jan 2004 (Median) to Mar 2025, with 251 observations. The data reached an all-time high of 859.000 RMB in May 2023 and a record low of 433.030 RMB in Jan 2004. Shanghai: Star-Rated Hotel: Average Room Rate data remains active status in CEIC and is reported by Shanghai Municipal Tourism Administration. The data is categorized under China Premium Database’s Hotel Sector – Table CN.QHRA: Star-Rated Hotel: Shanghai.

📌 Title: Hotel Booking Analysis Dashboard – Power BI

📌 Description: This Power BI dashboard provides an interactive analysis of hotel booking trends using historical booking data. It offers insights into total bookings, customer preferences, cancellations, and room allocation across different hotels.

🔹 Key Insights: ✅ Total Bookings: 119,386, with insights into lead time and average night stays. ✅ Booking Trends: Breakdown by year, month, and hotel type (City Hotel vs. Resort Hotel). ✅ Cancellations: Comparison of canceled bookings across different hotels. ✅ Customer Segments: Analysis by customer type, meal preferences, and market segments. ✅ Distribution Channels: Impact of booking sources on revenue and deposits. ✅ Parking & Room Types: Distribution of required car parking spaces and reserved vs. assigned room types.

📊 Technologies Used: Power BI for data visualization Excel/SQL for data preprocessing DAX for calculated measures 🔗 How to Use This Dashboard: Explore yearly and monthly booking trends to understand seasonality. Identify cancellation patterns to improve booking policies. Analyze market segments and customer behavior for targeted marketing. 🚀 If you find this dashboard insightful, leave feedback and connect with me! 😊

Context

A significant number of hotel bookings are called off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.

The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.

The cancellation of bookings impact a hotel on various fronts:

Loss of resources (revenue) when the hotel cannot resell the room. Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms. Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin. Human resources to make arrangements for the guests. Objective

The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Data Description

The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.

Data Dictionary

Booking_ID: unique identifier of each booking no_of_adults: Number of adults no_of_children: Number of Children no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel type_of_meal_plan: Type of meal plan booked by the customer: Not Selected – No meal plan selected Meal Plan 1 – Breakfast Meal Plan 2 – Half board (breakfast and one other meal) Meal Plan 3 – Full board (breakfast, lunch, and dinner) required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes) room_type_reserved: 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 arrival_year: Year of arrival date arrival_month: Month of arrival date arrival_date: Date of the month market_segment_type: Market segment designation. repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes) no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros) no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc) booking_status: Flag indicating if the booking was canceled or not.

Taiwan General Hotel: Average Daily Room Rate data was reported at 2,286.000 NTD in Dec 2017. This records an increase from the previous number of 2,107.000 NTD for Nov 2017. Taiwan General Hotel: Average Daily Room Rate data is updated monthly, averaging 2,080.000 NTD from Jan 2010 (Median) to Dec 2017, with 96 observations. The data reached an all-time high of 2,582.000 NTD in Feb 2016 and a record low of 1,763.000 NTD in May 2010. Taiwan General Hotel: Average Daily Room Rate data remains active status in CEIC and is reported by Tourism Bureau, Ministry of Transportation and Communications. The data is categorized under Global Database’s Taiwan – Table TW.Q015: Hotel Statistics: General Hotel: Average Daily Room Rates.

Colombia Average Room Rates: Suite data was reported at 139.431 Dec2004=100 in May 2020. This records a decrease from the previous number of 142.313 Dec2004=100 for Apr 2020. Colombia Average Room Rates: Suite data is updated monthly, averaging 143.572 Dec2004=100 from Dec 2004 (Median) to May 2020, with 186 observations. The data reached an all-time high of 157.429 Dec2004=100 in Jan 2020 and a record low of 100.000 Dec2004=100 in Dec 2004. Colombia Average Room Rates: Suite data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.Q002: Hotel Rates and Average Room Rate Index: 2005=100.

Colombia Average Room Rates: Single data was reported at 150.512 Dec2004=100 in May 2020. This records a decrease from the previous number of 164.339 Dec2004=100 for Apr 2020. Colombia Average Room Rates: Single data is updated monthly, averaging 146.278 Dec2004=100 from Dec 2004 (Median) to May 2020, with 186 observations. The data reached an all-time high of 179.992 Dec2004=100 in Jan 2020 and a record low of 100.000 Dec2004=100 in Dec 2004. Colombia Average Room Rates: Single data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.Q002: Hotel Rates and Average Room Rate Index: 2005=100.