predict probability of hotel booking incorporating length of stay - ideally daily Let us say I have daily room booking data over time. Rooms have features X like aircon, number of sleeps/person. Rooms can be booked for a number of nights (NONs) between 1 and 7. They also have a price per night and prices for shorter NONs tend to be larger to encourage longer stays. I would like to be able to predict the "conversion probability" of a room ideally at daily level:
P(Time, X, NONs, price per night)
At first, I thought this could be modelled using a multiclass/multinomial approach. However, I do not think this could work. Concerns:

*

*Successive days/weeks depend on the NONs of the previous day(s)

*A room can only be sold once but clearly (mutually exclusive at room level). However, for certain prices, there is a probability > 0 for different NONs (non mutually exclusive classification problem?).

Maybe I overthink this and there is some clever pragmatic simplification to my problem? I would appreciate any pointers on how I could tackle this please.

The biggest, practical simplification of the problem would be splitting it into two:

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*Predict if the room is going to be booked. It's a binary prediction that can be handled by one of the many algorithms for such problems.

*Predict the length of stay for the booked rooms. This is a regression problem where you predict time. The model is trained only on the data on rooms that got booked.

