# 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$)4\mathrm{.}10$ LAB: Prediction with Logistic Regression

The file Invistico$\_$Airline$\_$LR$.$csv contains information from an airline using the alias Invistico Airline on customer satisfaction, as well as

details on each customer. The columns of interest are Gender, Age, Class, Arrival$\_$Delay$\_$in$\_$Minutes, and satisfaction.

Read the file Invistico$\_$Airline$\_$LR$.$csv into a data frame.

Obtain user defined values female, age, economy, and delay.

Re$-$code the categorical variables Gender, Class, and satisfaction into dummy variables.

Create a new data frame X from the predictor variables Gender$\_$female, Age, Class$\_$Eco, and Arrival$\_$Delay$\_$in$\_$Minutes, in that order.

Create a response variable $Y$ from the dummy variable satisfaction$\_$satisfied.

Perform logistic regression on $x$ and $Y.$

Use the user defined values to predict the probability that a customer with those values is satisfied.

Ex: If the input is $134010$ the ouput is: