Prediction of Hotel Booking Cancellation Using Deep Neural Network and Logistic Regression Algorithm

Abstract

Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes

Customer Decision Prediction Using Deep Neural Network on Telco Customer Churn Data

“…Optimizer is an algorithm used for gradient optimization on neural networks such as weights and learning rates to reduce losses [30]. This study uses five optimizations derived from the Keras model, namely the optimizer stochastic gradient descent (SGD) is a deep learning model that optimizes functions by following a gradient that has nosy with decreasing step size [31], optimization algorithm adaptive moment estimation (Adam) which is an extension for SGD [32], adaptive gradient algorithm (Adagrade) is a modified stochastic gradient descent algorithm with a learning rate per parameter [30], Adadelta is a stochastic gradient descent method based on the dimension adaptive learning rate, and root mean square propagation (RMSProp) is a method in which the learning rate is adjusted for each parameter.…”