# import the necessary modules

female $=$ int$($input$())$

age $=$ float$($input$())$

economy $=$ int$($input$())$

delay $=$ float$($input$())$

flights $=$ # read in the file Invistico$\_$Airline$\_$LR$.$csv

# remove missing data

flights.dropna$($axis $=0,$ inplace $=$ True$)$

flights $=$ # recode the categorical variables Gender, Class, and satisfaction as dummy variables

X $=$ # create a new data frame from the variables Gender$\_$Female, Age, Class$\_$Eco, and Arrival$\_$Delay$\_$in$\_$Minutes, in that order.

X $=$ add$\_$constant$($X$)$

Y $=$ # set Y as the response variable satisfaction$\_$satisfied

model $=$ # perform logistic regression on X and Y

ex $=$ # create an array with $1$ for the intercept, and the user input values female, age, economy, and delay

prediction $=$ # find the predicted probablility that a customer with the user input values is satisfied

print$($prediction$)$