An Intelligent Hotel Booking Cancellation Prediction System Using Machine Learning Techniques

Abstract

The rapid growth of the hospitality industry has led to an increase in hotel booking
activities, making cancellation prediction a critical task for efficient resource management.
Frequent booking cancellations result in significant revenue loss and operational
inefficiencies for hotels. To address this issue, this paper presents an intelligent hotel
booking cancellation prediction system using advanced machine learning techniques. The
system aims to accurately predict whether a booking will be canceled or not, enabling hotel
management to make informed decisions and optimize their operations.
The proposed system utilizes a dataset containing various attributes related to hotel
bookings, such as customer details, reservation status, and booking characteristics. Data
preprocessing is performed to handle missing values, remove irrelevant features, and
convert categorical variables into numerical representations. Feature engineering
techniques are applied to improve model performance and ensure data consistency.
Multiple machine learning models, including Logistic Regression, Decision Tree, Random
Forest, XGBoost, and LightGBM, are implemented and evaluated. Each model is trained
using a portion of the dataset and tested on unseen data to assess its predictive accuracy.
Performance metrics such as accuracy and F1-score are used to compare the effectiveness
of different models. Among these, ensemble models like Random Forest and boosting
algorithms such as XGBoost and LightGBM demonstrate superior performance due to their
ability to handle complex data patterns.