Predicting Airline Passenger Satisfaction with Classification Algorithms
Dublin Core
Title
Predicting Airline Passenger Satisfaction with Classification Algorithms
Subject
Classification, Airlane, Satisfaction, Predicting, Random Forest
Description
Airline businesses around the world have been destroyed by Covid-19 as most international air travel has been banned. Almost
all airlines around the world suffer losses, due to being prohibited from carrying out aviation transportation activities which are
their biggest source of income. In fact, several airlines such as Thai Airways have filed for bankruptcy. Nonetheless, after the
storm ends, demand for air travel is expected to spike as people return for holidays abroad. The research is aimed at analyzing the
competition in the aviation industry and what factors are the keys to its success. This study uses several classification models
such as KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest which will later be compared. The results
of this study get the Random Forest Algorithm using a threshold of 0.7 to get an accuracy of 99% and an important factor in
getting customer satisfaction is the Inflight Wi-Fi Service.
all airlines around the world suffer losses, due to being prohibited from carrying out aviation transportation activities which are
their biggest source of income. In fact, several airlines such as Thai Airways have filed for bankruptcy. Nonetheless, after the
storm ends, demand for air travel is expected to spike as people return for holidays abroad. The research is aimed at analyzing the
competition in the aviation industry and what factors are the keys to its success. This study uses several classification models
such as KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest which will later be compared. The results
of this study get the Random Forest Algorithm using a threshold of 0.7 to get an accuracy of 99% and an important factor in
getting customer satisfaction is the Inflight Wi-Fi Service.
Creator
B.Herawan Hayadi 1,*, Jin-Mook Kim 2, Khodijah Hulliyah 3, Husni Teja Sukmana 4
Date
2021
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
B.Herawan Hayadi 1,*, Jin-Mook Kim 2, Khodijah Hulliyah 3, Husni Teja Sukmana 4, “Predicting Airline Passenger Satisfaction with Classification Algorithms,” Repository Horizon University Indonesia, accessed June 28, 2025, https://repository.horizon.ac.id/items/show/9252.