HOTEL BOOKING CANCELLATION PATTERN ANALYSIS AND SEASONAL DEMAND FLUCTUATION PROFILING USING PYTHON

Abstract

The Hotel Booking Cancellation Pattern Analysis and Seasonal Demand Fluctuation Profiling Using Python project focuses on analyzing hotel booking behavior, identifying cancellation trends, and understanding seasonal demand variations in the hospitality industry using data analytics and machine learning techniques. In the modern hospitality sector, booking cancellations and fluctuating customer demand significantly affect hotel revenue, occupancy rates, pricing strategies, and operational planning. Traditional analysis methods mainly rely on manual reporting and basic statistical techniques, which are often time-consuming and less effective when handling large-scale booking datasets. This project addresses these challenges by utilizing intelligent data analytics methods to uncover meaningful patterns in hotel booking behavior and support data-driven decision-making. The proposed system utilizes historical hotel booking datasets containing information such as booking dates, customer demographics, lead time, market segments, room types, average daily rates, reservation status, cancellation history, and seasonal demand patterns. Data preprocessing techniques including handling missing values, normalization, feature encoding, and feature selection are applied to improve data quality, consistency, and analytical performance before conducting analysis. Exploratory Data Analysis (EDA) techniques are implemented to identify trends, correlations, and hidden relationships between variables influencing booking cancellations and occupancy rates. Time-series analysis is used to detect seasonal booking patterns, peak tourism periods, holiday demand fluctuations, and off-season occupancy variations. Visualization techniques such as graphs, heatmaps, line charts, bar charts, and dashboards are used to represent booking behavior, cancellation rates, customer segments, and seasonal trends clearly for better interpretation.