Authors: S. Akhila, A. Ruchitha
Abstract: Because they affect occupancy rates, revenue output, and operational planning, reservation cancellations are becoming an increasingly big issue for the hospitality industry. If hotel management can anticipate potential cancellations in advance of the scheduled check-in date, they may put effective reservation strategies in place and make informed decisions. This research presents a machine learning-based system for predicting hotel booking cancellations by assessing reservation-related variables such as booking lead time, guest data, room pricing, market segment, booking history, and special requests. The first step is to apply Exploratory Data Analysis (EDA) on the booking dataset to identify the factors that have a major influence on non-payment. Next, four supervised ML methods are trained and evaluated using various performance metrics, such as F1-score, recall, accuracy, and precision. These algorithms are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). When compared to the other models, the Random Forest classifier performs the best in terms of prediction accuracy and total classification performance. Customers will find it much easier to understand the forecast results with the help of an interactive visual analytics tool that shows booking trends, cancellation details, and model comparisons. The proposed solution aids hotel management in a number of ways, including intelligent cancellation prediction, better reservation administration, and data-driven commercial decision-making. The decision-support capabilities of machine learning techniques have also been shown in related disciplines [1, 2].
DOI: http://doi.org/10.5281/zenodo.21374503
