Loyalty Status of Hotel Guests: Simulated Dataset for Predicting Customer Loyalty

This dataset consists of 2,000 simulated records of hotel guests, designed for customer loyalty prediction based on several key features. Each guest's loyalty status ("Yes" for loyal, "No" for non-loyal) indicates their eligibility for loyalty discounts, with "loyal" signifying that the individual can enjoy these discounts. The dataset includes factors such as frequency of bookings, days since the last booking, total revenue generated, average stay duration, and total meal charges.

The dataset was generated using Python with the Faker library to create realistic guest names and emails, featuring names in different languages to reflect a diverse international perspective. The countries represented include India, the United States, the United Kingdom, France, Germany, Spain, Italy, Japan, and China.

A Python code file is provided alongside this dataset for those interested in the methodology used to create it. This resource serves as a practical tool for exploring machine learning techniques and data analysis in the hospitality sector.

We are building this dataset as part of a dynamic pricing project for hotels, aiming to enhance decision-making through customer segmentation.