In the hotel industry, cancellations represent a revenue management challenge, as hotels find themselves at risk of having empty rooms when a customer cancels a reservation, in addition to the cost of honoring existing reservations. The aim of this notebook is to develop a predictive model to reduce the cancellation rate from 42% to 20%.
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In the hotel industry, cancellations represent a revenue management challenge, as hotels find themselves at risk of having empty rooms when a customer cancels a reservation, in addition to the cost of honoring existing reservations. The aim of this notebook is to develop a predictive model to reduce the cancellation rate from 42% to 20%.
realdataforbreakfast/Predict_Hotel_Booking_Cancellations
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In the hotel industry, cancellations represent a revenue management challenge, as hotels find themselves at risk of having empty rooms when a customer cancels a reservation, in addition to the cost of honoring existing reservations. The aim of this notebook is to develop a predictive model to reduce the cancellation rate from 42% to 20%.
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