Predictive models developed by applying Data Mining techniques are used to improve forecasting accuracy in the airline business. In order to maximize the revenue on a flight, the number of seats available for sale is typically higher than the physical seat capacity (overbooking). To optimize the overbooking rate, an accurate estimation of the number of no-show passengers (passengers who hold a valid booking but do not appear at the gate to board for the flight) is essential. Currently, no-shows on future flights are estimated from the number of no-shows on historical flights averaged on booking class level. In this work, classification trees and logistic regression models are applied to estimate the probability that an individual passenger turns out to be a no-show. Passenger information stored in the reservation system of the airline is either directly used as explanatory variable or used to create attributes that have an impact on the probability of a passenger to be a no-show. The total number of no-shows in each booking class or on the total flight is then obtained by accumulating the individual no-show probabilities over the entity of interest. We show that this forecasting approach is more accurate than the currently used method. In addition, the selected models lead to a deepened insight into passenger behavior.