Hotel booking cancellation and machine learning

In recent years, machine learning has emerged as a powerful tool with widespread applications across various domains due to its ability to process and analyze vast amounts of data. This study explores the application of machine learning techniques in predicting hotel booking cancellations using Property Management System (PMS) data. The research involves a comprehensive process, including data cleaning, feature engineering, feature selection, and model development. Feature selection and dimensionality reduction using Principal Component Analysis (PCA) and Lasso regression identified key predictive features, facilitating the rapid creation of neural network models. A diverse set of machine learning and deep learning models, such as Logistic Regression, Decision Tree, Random Forest, XGBoost, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM), were employed. All models achieved accuracies exceeding 80%, with neural networks nearing 100%. These results highlight the efficacy of these models in predicting cancellations across different hotels, revealing consistent cancellation patterns. The study demonstrates the potential of machine learning to optimize hotel management by accurately forecasting booking cancellations, thereby reducing uncertainty and increasing revenue. Future work may focus on exploring more advanced feature engineering techniques and models to further enhance prediction accuracy and generalizability.