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A User-friendly and Powerful R Analysis of Large-scale Datasets

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Hotel booking demand datasets.

Nuno Antonio^1,2, Ana de Almeida^1,3,4, Luis Nunes^1,2,4

¹Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal.

|December 25, 2018

Summary

This data article presents two real-world hotel booking datasets, one for a resort hotel and one for a city hotel. These datasets are valuable for research in revenue management and machine learning.

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Area of Science:

Hospitality Management
Data Science
Machine Learning

Background:

Real-world business data is scarce for academic research.
Hotel industry generates vast amounts of booking data.
Existing datasets often lack detail or are proprietary.

Purpose of the Study:

To provide two comprehensive, anonymized datasets of hotel booking information.
To support research and education in revenue management, data mining, and machine learning.
To enable the development and testing of predictive models using real hotel data.

Main Methods:

Data collection from two distinct hotel types: resort (H1) and city (H2).
Data structuring with 31 variables per observation (booking).
Anonymization of all personal and hotel identification details.

Main Results:

Dataset H1 contains 40,060 observations; Dataset H2 contains 79,330 observations.
Both datasets cover bookings from July 1, 2015, to August 31, 2017.
Includes both confirmed arrivals and cancellations, offering a complete demand picture.

Conclusions:

The datasets offer a valuable resource for the scientific community.
Facilitates empirical research in revenue management and data-driven decision-making.
Contributes to advancing machine learning and data mining applications in the hospitality sector.