Reservation Cancellation Predictions of Hotels

Keywords: Reservation, Cancellation

Background

Online hotel reservation channels have transformed customer behavior and booking options. However, they have also led to a high number of cancellations and no-shows, which can be attributed to factors like scheduling conflicts and changing plans. While the flexibility of free or low-cost cancellations benefits guests, it poses challenges for hotels as it may impact their revenue. Challenges in Reservation Cancellation Predictions using Artificial Neural Network projects include obtaining comprehensive training data and balancing accuracy in classification. Additionally, ensuring scalability and real-time performance for handling large volumes of reservation data is crucial. Developing an accurate artificial neural network model to predict hotel reservation cancellations, considering various factors such as customer behavior, external events, and booking patterns. Methods include data preprocessing, designing the model architecture, training the network using optimization algorithms, tuning hyperparameters, evaluating performance, and using the trained model for predictions and deployment. After training and testing the model and running the 50 epochs, the validation accuracy is 75% while accuracy is 72% whereas the actual loss is 0.5389 and validation loss is 0.5380

Aim & Objectives

Aim
To predict the Reservation or Cancellation of the Hotel using the DL methods
Objective
Implement an ANN model to predict hotel reservation cancellations, optimizing resource allocation and revenue management.
Develop an ANN system that considers booking history and external factors for accurate cancellation forecasts.
Evaluate the ANN's prediction performance, aiming to minimize revenue loss and enhance hotel operation efficiency.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

— Preview truncated. The complete document (full chapters, references, diagrams, and appendices) is shared with clients as part of project delivery. —