Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets

Pujo Hari Saputro, Herlino Nanang

Abstract


Online ordering is the latest breakthrough in the hospitality industry, but when it comes to booking cancellations, it has a negative impact on it. To reduce and anticipate an increase in the number of booking cancellations, we developed a booking cancellations prediction model using machine learning interpretable algorithms for hotels. Both models used Random Forest and the Extra Tree Classifier share the highest precision ratios, Random Forest on the other hand has the highest recall ratio, this model predicted 79% of actual positive observations. These results prove that it is possible to predict booking cancellations with high accuracy. These results can also help hotel owners or hotel managers to predict better predictions, improve cancellation regulations, and create new tactics in business.

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Keywords


EDA; Data Analysis; Booking Cancelation; Random Forest; Extra Tree Classifier

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Journal of Applied Data Sciences

ISSN : 2723-6471 (Online)
Organized by : Departement of Information System, Universitas Amikom Purwokerto, Indonesia; Computer Science and Systems Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia.
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