Project introduction: The cancellation rate for booking hotels online is high that creates discomfort for many hotels and create a desire to take precautions. Therefore, predicting reservations that can be cancelled will create a surplus value for hotels and hotels can take action to prevent these cancellations. In my final project, I will try to explore the dataset and explain how to predict future cancelled reservations in advance by machine learning methods.
Kaggle describes this dataset as follows: Have you ever wondered when the best time of year to book a hotel room is? Or the optimal length of stay in order to get the best daily rate? What if you wanted to predict whether or not a hotel was likely to receive a disproportionately high number of special requests? This hotel booking dataset can help you explore those questions! This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. All personally identifying information has been removed from the data. The data is originally from the article Hotel Booking Demand Datasets, written by Nuno Antonio, Ana Almeida, and Luis Nunes for Data in Brief, Volume 22, February 2019. The data was downloaded and cleaned by Thomas Mock and Antoine Bichat for #TidyTuesday during the week of February 11th, 2020.