Comparison of Machine Learning Algorithms for Hotel Booking Cancellation in Automated Method

Abstract

People usually book hotels online and cancel booking due to various reasons, which is a loss to the business, which is an important problem for hotel managers. These articles examined how artificial intelligence is used to determine which reservations can be cancelled and therefore avoid some losses. The study compares algorithms that forecast cancellation, such as Decision Trees, Naive Bayes, KNN, Logistic Regression, and Random Forest. The data is obtained from the publicly available dataset. Lots of insights from the data can be fetched and pre-processing, feature encoding and engineering is applied. This method is carried out to develop a model which gives minimum error and good accuracy in an automated process.