In my research endeavor, the primary goal is to employ advanced machine learning algorithms, particularly XGBoost, to discern patterns that indicate the likelihood of hotel booking cancellations. This algorithmic approach enables us to unravel complex relationships within the dataset, providing a deeper understanding of the factors influencing customer decisions. By predicting the probability of cancellations, hotels gain a valuable tool for proactive decision-making and resource allocation.

The benefits of employing such algorithms in the hotel industry are manifold. Firstly, hotels can optimize their operational efficiency by anticipating potential cancellations and adjusting staffing, inventory, and other resources accordingly. This not only helps in minimizing revenue losses but also enhances customer satisfaction by ensuring a seamless and well-managed experience.

Furthermore, the insights derived from the model empower hotels to implement targeted strategies for customer engagement and retention. By identifying specific features and behaviors associated with booking cancellations, hotels can tailor their marketing, pricing, and customer service initiatives to mitigate the risk of cancellations and foster guest loyalty.

In essence, my algorithm serves as a powerful decision support tool for the hotel industry, enabling proactive measures that contribute to improved financial performance and enhanced guest satisfaction.

ps:You can also use the algorithm from the page I created with the link :https://cancelationpredictor.streamlit.app/