with Covid winding down more and more people are traveling again and airline customer satisfactions are greatly decreasing, due to the airports being overcrowded and rising ticket prices. According to CNN "decrease in food and beverage satisfaction in premium economy and business and the fact that many airlines didn’t serve alcohol on board for much of last year".
I hope to determine and find out if higher class plane tickets leads to more satisfied passengers, and create a predictor based on plane attributes to determine the passenger satisfaction.
I will use the Kaggle Airline Passenger Satisfaction Data Set to gather statistics about each passenger fight.
Here is my data dictionary
Gender: Gender of the passengers (Female, Male)
Customer Type: The customer type (Loyal customer, disloyal customer)
Age: The actual age of the passengers
Type of Travel: Purpose of the flight of the passengers (Personal Travel, Business Travel)
Class: Travel class in the plane of the passengers (Business, Eco, Eco Plus)-
Flight distance: The flight distance of this journey
Inflight wifi service: Satisfaction level of the inflight wifi service (0:Not Applicable;1-5)
Departure/Arrival time convenient: Satisfaction level of Departure/Arrival time convenient
Ease of Online booking: Satisfaction level of online booking
Gate location: Satisfaction level of Gate location
Food and drink: Satisfaction level of Food and drink
Online boarding: Satisfaction level of online boarding
Seat comfort: Satisfaction level of Seat comfort
Inflight entertainment: Satisfaction level of inflight entertainment
On-board service: Satisfaction level of On-board service
Leg room service: Satisfaction level of Leg room service
Baggage handling: Satisfaction level of baggage handling
Check-in service: Satisfaction level of Check-in service
Inflight service: Satisfaction level of inflight service
Cleanliness: Satisfaction level of Cleanliness
Departure Delay in Minutes: Minutes delayed when departure
Arrival Delay in Minutes: Minutes delayed when Arrival
Satisfaction: Airline satisfaction level(Satisfaction, neutral or dissatisfaction)
import pandas as pd
df_trips = pd.read_csv("train.csv")
df_trips.head()
| Unnamed: 0 | id | Gender | Customer Type | Age | Type of Travel | Class | Flight Distance | Inflight wifi service | Departure/Arrival time convenient | ... | Inflight entertainment | On-board service | Leg room service | Baggage handling | Checkin service | Inflight service | Cleanliness | Departure Delay in Minutes | Arrival Delay in Minutes | satisfaction | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 70172 | Male | Loyal Customer | 13 | Personal Travel | Eco Plus | 460 | 3 | 4 | ... | 5 | 4 | 3 | 4 | 4 | 5 | 5 | 25 | 18.0 | neutral or dissatisfied |
| 1 | 1 | 5047 | Male | disloyal Customer | 25 | Business travel | Business | 235 | 3 | 2 | ... | 1 | 1 | 5 | 3 | 1 | 4 | 1 | 1 | 6.0 | neutral or dissatisfied |
| 2 | 2 | 110028 | Female | Loyal Customer | 26 | Business travel | Business | 1142 | 2 | 2 | ... | 5 | 4 | 3 | 4 | 4 | 4 | 5 | 0 | 0.0 | satisfied |
| 3 | 3 | 24026 | Female | Loyal Customer | 25 | Business travel | Business | 562 | 2 | 5 | ... | 2 | 2 | 5 | 3 | 1 | 4 | 2 | 11 | 9.0 | neutral or dissatisfied |
| 4 | 4 | 119299 | Male | Loyal Customer | 61 | Business travel | Business | 214 | 3 | 3 | ... | 3 | 3 | 4 | 4 | 3 | 3 | 3 | 0 | 0.0 | satisfied |
5 rows × 25 columns
This data set includes basic passenger information, flight information, and their ratings about the flight. Therefore it has all the qualifications as the base for making a predictor for passengers satisfaction levels for future plane rides.
I will group the passengers into sets of classes based on their class of purchase. Doing so I hope to discover if there are any trends among their overall satrisfaction level, in order to build a satisfaction predictor I would have to determine the weights of customers satisfaction rating and customer characteristics on the overall end result.