Hotel Booking Analysis and Prediction Using Data Mining

Abstract

The project aims to analyze hotel booking data using various mining algorithms to extract meaningful patterns and insights. The dataset includes customer demographics, booking dates, room preferences, and cancellation history. Subsequently, preprocessing techniques such as data cleaning, normalization, and feature selection are employed to prepare the data for modelling. The insights derived from this analysis can assist hotel management in optimizing pricing strategies, enhancing customer segmentation, and implementing proactive measures to reduce booking cancellations. Additionally, the findings contribute to a deeper understanding of customer behaviour and preferences in the hotel booking domain, ultimately leading to improved service delivery and customer satisfaction. Several data mining algorithms, including but not limited to decision trees, association rule mining, clustering, and neural networks, are applied to uncover patterns related to booking patterns, customer segmentation, and cancellation predictors.
By understanding customer behaviour and preferences, hotels can enhance customer satisfaction, optimize resource utilization, and maximize revenue.