Optimizing Hotel Efficiency Through Predictive Analysis of Reservation Cancellations

One of the most essential in hotel operational efficiency is to enhance profitability and customer satisfaction. Handling reservation cancellations is a vital part corresponding with the operational efficiency, which can have an effect on revenue management, resource allocation, and even overall service quality. By identifying underlying factors that contribute to cancellations and developing predictive models to alleviate their impact, this research explores how analyzing cancellation patterns can improve hotel efficiency. Hotels can have a prediction of cancellations more accurately, and furthermore, adjusting booking policies, and implement dynamic pricing strategies by analyzing historical data and applying classification models like Logistic Regression, Decision Tree and Random Forest. The function of flexible booking options, customer segmentation, and targeted communication to reduce last-minute cancellations by analyzing classification models is highlighted in the research. Ultimately, this method improves resource utilization, minimizes revenue loss, and improves the overall customers experience.