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4.10 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 1 34 0 10 the ouput is:

Here is the code thus far

import the necessary modules import numpy as np import statsmodels.api as sm import pandas as pd

female = int(input()) age = float(input()) economy = int(input()) delay = float(input())

flights = pd.read_csv("Invistico_Airline_LR.csv") # read in the file Invistico_Airline_LR.csv

# remove missing data flights.dropna(axis = 0, inplace = True)

flights =pd.get_dummies(flights, columns=['satisfaction', 'Gender','Class']) # recode the categorical variables Gender, Class, and satisfaction as dummy variables

# create a new data frame from the variables Gender_Female, Age, Class_Eco, and Arrival_Delay_in_Minutes, in that order. X = flights[['Gender_Female', 'Age', 'Class_Eco', 'Departure_Delay_in_Minutes']] X = sm.add_constant(X) Y = flights[['satisfaction_satisfied']] # set Y as the response variable satisfaction_satisfied

model = sm.Logit(Y,X) # perform logistic regression on X and Y model = model.fit()

ex = np.array([1, female, age, economy, delay]) # create an array with 1 for the intercept, and the user input values female, age, economy, and delay

# find the predicted probablility that a customer with the user input values is satisfied prediction = model.predict(ex)

print(prediction)

Program errors displayed here Traceback (most recent call last): File "main.py", line 6 , in female = int (input()) ValueError: invalid literal for int() with base 10: ' 1 34 10