What is the correct approach in this case for modelling data (logistic regression) I'm making a logistic regression model but am unsure about whether it is right or not to do the following:
I'm trying to predict if a person will buy a high cost hotel, given by hotel_spend > 250. I know columns such as flight_spend and vehicle_spend are acceptable inputs to the model but am unsure if I could use total_spend as it contains information about hotel_spend which is used to create the target. This is highlighted by the last row where (hotel_spend == total_spend) > 250. My head tells me I shouldn't do this as I'm using the hotel_spend to predict if they will spend a certain amount on a hotel. 
I'm looking for advice if this is acceptable or not. In my head I don't think should be done, just looking for other opinions.
flight_spend  hotel_spend  vehicle_spend  total_spend  \
     20           49             33          102   
      0           59              0           59   
     65          100             40          205   
    150          250             50          450   
      0          300              0          300   
hotel_spend_high_spend_label  
           0  
           0  
           0  
           1  
           1  

 A: You shouldn't use any variables that you won't know at the time of prediction. Since you won't know flight_spend when you make predictions (since it is what you're predicting) and 
total_spend = flight_spend + hotel_spend + vehicle_spend
you also won't know total_spend. In general, don't use a variable that depends on the value of what you're predicting.
I wrote a blog post that may help elaborate the issue: https://content.nexosis.com/blog/ensuring-data-is-ready-for-machine-learning-algorithms
A: Correct, you shouldn't use total_spend because as you say in a sense that variable already encapsulates the response variable you are trying to model.  However, it is fine to use flight_spend and vehicle_spend if your purpose is to understand decision-making and the relationship between spend types.  
BTW, my experience with data like this is that the relationships will be non-linear you will need to transform the explanatory variables in some fashion.  As you have numerous values of zero, you don't want to do this by taking logarithms.  A square root, or more generally a Box-Cox-like transformation is one relatively straightforward way of dealing with this; an alternative would be a generalized additive model with splines or similar based on flight and vehicle spend.
