Enhancing Hospitality Management Through ML-Based Cancellation Prediction

Hotel booking cancellations create significant challenges for the hospitality industry by affecting revenue management, demand forecasting, and resource allocation. Traditional prediction methods often fail to capture the complex behavioral patterns associated with customer cancellations. This research proposes a machine learning-based approach using a Multi-Layer Perceptron (MLP) classifier to predict hotel booking cancellations accurately. The model analyzes various booking attributes such as lead time, room type, customer segment, location, and booking history to detect patterns associated with cancellation behavior. Data preprocessing techniques including normalization, handling missing values, and encoding categorical variables are applied to improve model performance. Hyperparameter tuning is performed to optimize hidden layers, activation functions, and learning rates. Experimental results demonstrate that the proposed model provides high prediction accuracy and supports hotel managers in making informed decisions related to demand forecasting, overbooking strategies, and revenue optimization. The system ultimately contributes to improved operational efficiency and customer satisfaction in the hospitality sector. Hotel booking cancellations create major challenges for the hospitality industry by affecting revenue management and demand forecasting. This research proposes a machine learning approach using a Multi-Layer Perceptron (MLP) classifier to predict hotel booking cancellations based on historical booking data. The model helps hotel managers improve decision-making, optimize resources, and reduce financial losses caused by cancellations.