AIRLINE PASSENGER SATISFACTION PREDICTION

Project Report Submitted in Partial fulfilment of the requirement for

the award of Degree of

BACHELOR OF BUSINESS ADMINISTRATION (BBA)

Submitted by
H AMRUTHA CHOWDARY
Reg. No: 22CBBBA012
Under the guidance of
DR.AYYAPPAN SIVASUBRAMANIAM

SCHOOL OF MANAGEMENT
CMR UNIVERSITY
April-2025

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 DECLARATION BY THE STUDENT

I H Amrutha Chowdary bearing Reg. No 22CBBBA012 hereby declares that this project
report entitled AIRLINE PASSENGER SATISFACTION PREDICTION has been
prepared by me towards the partial fulfilment of the requirement for the award of the
Bachelor of Business Administration (BBA) Degree under the guidance of DR. Ayyappan
Sivasubramaniam.

I also declare that this project report is my original work and has not been previously
submitted for the award of any Degree, Diploma, Fellowship, or other similar titles.

Signature
H Amrutha Chowdary
Reg. No: 22CBBBA012

Place: Bangalore

Date:

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 CERTIFICATE

Certified that this project report titled “AIRLINE PASSENGER SATISFACTION

PREDICTION” is the bonafide work of “H Amrutha Chowdary” who carried out the project
work under my supervision in the partial fulfillment of the requirements for the award of the BBA
degree.

SIGNATURE
DR. Ayyappan Sivasubramaniam.

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 Acknowledgement

Hereby, I represent to the utmost gratitude, all those that took part in the completion of this
capstone project's finalisation. Firstly, and foremost, I would like to thank DR. Ayyappan
Sivasubramaniam., my supervisor, for her important mentorship, guidance and her insightful
feedback, which during this research time I really enjoyed.

The completion of this work would not be without the participants and voters representing
both individuals and organisations that have been crucial to the success of this work. Through
the journey of this experience, although unavoidable, any errors or weaknesses that occur
might justly be attributed to me. But my gratitude will be extended from the bottom of my
heart to everyone who chipped in, no matter how large or small, to the success of these
endeavour

I am grateful to the various airline professionals and passengers who participated in the
surveys and interviews, sharing their experiences and insights, which provided critical data
for this research.

Lastly, I am grateful to my family for their unwavering support and encouragement. Their
belief in my abilities has been a source of strength throughout this project. Thank you all for
being a part of this journey.

This project would not have been possible without the collective support of all these
individuals, and I am deeply appreciative of their contributions.

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 Abstract
Customer satisfaction plays a crucial role in the airline industry, influencing customer loyalty,
brand reputation, and overall business success. This study aims to analyze various factors
affecting airline passenger satisfaction, using data-driven techniques and visualizations.

The analysis involves exploring multiple aspects of passenger experience, including in-flight
services (Wi-Fi, food and drink, seat comfort), customer type, and flight distance. By
utilizing Python libraries such as Pandas, Seaborn, and Matplotlib, we investigate
correlations between these factors and overall satisfaction levels. Key statistical methods,
including grouped descriptive analysis, count plots, and box plots, help uncover meaningful
trends in passenger feedback.

Our findings suggest that passenger satisfaction is significantly influenced by service quality
factors like Wi-Fi availability and seat comfort. Loyal customers tend to have higher
satisfaction rates compared to disloyal customers. Additionally, variations in food and drink
quality also impact overall satisfaction. The visualizations provide clear insights into how
different passenger groups perceive airline services, helping airlines identify areas for
improvement.

This study serves as a valuable resource for airlines seeking to enhance their services,
improve customer retention, and optimize their operational strategies. Future work could
incorporate machine learning techniques to predict satisfaction levels based on passenger
demographics and travel preferences.

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 Table of Contents

CHAPTER 1 – INTRODUCTION 7
1.1 Introduction 7
CHAPTER 2 – LITERATURE REVIEW 10
2.1 Introduction 10
CHAPTER 3 – RESEARCH METHODOLOGY 14
3.1 Research Method 14
3.2 Sampling 16
3.3 Data Collection 17
3.3.1 Types of Data 17
3.3.2 Methods of Data Collection 18
CHAPTER 4 – DATA ANALYSIS & INTERPRETATION 20
CHAPTER 5 – FINDINGS, CONCLUSIONS & RECOMMENDATIONS 44
5.1 Findings 44
5.2 Conclusion 48
5.3 Recommendations 51
CHAPTER 6 – LIMITATIONS AND SCOPE OF FUTURE RESEARCH 55
6.1 Limitation 55
6.2 Scope of Future Research 57
Bibliography 59
Appendix – Questionnaires 60

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 CHAPTER 1 – INTRODUCTION
1.1 Background
The airline industry plays a vital role in global transportation, connecting people and
businesses across different regions. As air travel continues to grow, understanding and
improving passenger satisfaction has become a top priority for airlines. Passenger satisfaction
is influenced by multiple factors, including flight punctuality, baggage handling, inflight
services, seat comfort, and overall customer experience.
Traditionally, airlines have relied on customer surveys and feedback to assess satisfaction
levels. However, these methods are often time-consuming, prone to biases, and do not
provide real-time insights. The rise of machine learning (ML) and data analytics has enabled
the aviation industry to analyze large volumes of passenger data, identify patterns, and
predict satisfaction levels with greater accuracy.
This project, Airline Passenger Satisfaction Prediction, aims to apply machine learning
algorithms to classify passengers as either satisfied or dissatisfied based on various
travelrelated attributes. The insights derived from this study can help airlines enhance service
quality, improve customer retention, and optimize their operational strategies.

1.2 Problem Statement

Passenger satisfaction is a crucial determinant of airline success. A dissatisfied passenger may
not only avoid booking future flights with the airline but may also leave negative reviews,
affecting the airline’s reputation and profitability.
Current challenges faced by airlines in evaluating passenger satisfaction include:
1. Subjectivity of customer feedback – Passenger perceptions vary widely, making it
difficult to standardize satisfaction assessment.
2. Manual data analysis – Airlines often rely on customer service reports and surveys,
which are Labor - intensive and not scalable.

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 3. Lack of predictive capabilities – Airlines typically address complaints after they occur
rather than proactively identifying dissatisfaction factors.
By implementing a machine learning-based predictive model, this project aims to address
these challenges and help airlines take data-driven actions to improve customer experience.

1.3 Importance of Passenger Satisfaction in the Airline Industry

Passenger satisfaction directly impacts an airline's brand image, financial performance, and
customer loyalty. Some key reasons why it is essential include:
• Customer Retention: Satisfied passengers are more likely to book repeat flights and
remain loyal to the airline.
• Brand Reputation: Positive experiences lead to favorable online reviews and
recommendations, increasing the airline’s credibility.
• Revenue Growth: Higher satisfaction encourages passengers to opt for premium
services and upgrades, leading to increased revenue.
• Operational Efficiency: Understanding passenger dissatisfaction allows airlines to
optimize flight schedules, baggage handling, and customer service.
• Competitive Advantage: Airlines that prioritize customer satisfaction stand out in a
competitive market.
This project explores how machine learning can quantify and predict passenger satisfaction,
allowing airlines to make proactive service improvements.

1.4 Objectives of the Study

This project is designed to achieve the following objectives:
1. To analyze key factors influencing airline passenger satisfaction, such as inflight
services, seat comfort, baggage handling, and customer service.
2. To develop a machine learning model capable of predicting passenger satisfaction
based on structured airline survey data.
3. To compare different classification algorithms (e.g., logistic regression, decision trees,
random forest, support vector machines) and determine the most accurate model.
4. To provide actionable insights for airlines to improve customer service and enhance
overall passenger experience.
5. To create a dashboard for visualizing passenger satisfaction trends, helping airlines
make informed, data-driven decisions.

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1.5 Scope of the Project
This project focuses on airline passenger survey data analysis to develop a predictive model
for satisfaction classification. The scope includes:
1.5.1 Data Collection & Preprocessing
• The dataset consists of passenger demographics, flight details, service ratings, and
overall satisfaction levels.
• Data preprocessing steps include handling missing values, encoding categorical data,
feature selection, and data normalization.
1.5.2 Machine Learning Techniques
• The study evaluates multiple classification models, including:
o Logistic Regression – for binary classification. o Decision Trees &
Random Forest – for feature importance analysis. o Support Vector
Machine (SVM) – for advanced classification.
o Neural Networks – for deep learning-based predictions.
• Model performance is evaluated using accuracy, precision, recall, and F1-score to
determine the best predictive model.
1.5.3 Data Analysis & Insights
• Exploratory Data Analysis (EDA) is performed using Python, Tableau, and Power BI
to identify trends and correlations.
• The study helps determine which factors have the greatest impact on passenger
satisfaction.
1.5.4 Industry Applications
The insights from this study can help airlines:
• Enhance customer experience by addressing dissatisfaction factors.
• Optimize flight operations by reducing delays and improving service quality.
• Develop targeted marketing strategies based on passenger preferences.
• Improve loyalty programs through data-driven customer segmentation.
1.5.5 Limitations of the Study
• Dataset Constraints: The dataset may not fully represent all passenger preferences, as
satisfaction varies based on airline type, region, and travel class.
• External Factors: Real-time influences like weather conditions, economic factors, and
airline policies are not covered in this study.

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 • Static vs. Real-Time Data: The project is based on historical survey data, which may
not capture real-time passenger sentiments.

CHAPTER 2 – LITERATURE REVIEW

2.1 Introduction
Introduction
Passenger satisfaction is a pivotal aspect of the airline industry, significantly influencing
customer loyalty and competitive advantage. As airlines strive to differentiate themselves in a
crowded market, understanding the factors that contribute to passenger satisfaction has
become increasingly important. This literature review synthesizes key findings from various
studies, focusing on service quality, passenger experience, predictive modeling, specific
satisfaction factors, and the role of feedback.

Service Quality:
Service quality has been widely recognized as a fundamental driver of passenger satisfaction.
The SERVQUAL model identifies five critical dimensions of service quality: tangibles,
reliability, responsiveness, assurance, and empathy. Each dimension plays a unique role in
shaping customer perceptions and experiences.

Tangibles refer to the physical aspects of the service, such as cleanliness and comfort of the
aircraft. Airlines that invest in maintaining clean, modern, and comfortable aircraft often
report higher levels of passenger satisfaction. This includes not only the aircraft's interior but
also the amenities offered, such as entertainment systems and Wi-Fi access. Reliability
encompasses the airline's ability to perform promised services consistently, while
responsiveness focuses on the willingness to help passengers and provide prompt service.
Airlines that promptly address customer inquiries and complaints demonstrate a commitment
to service excellence, which fosters trust and satisfaction.

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Assurance pertains to the knowledge and courtesy of staff, fostering a sense of trust, and
empathy reflects the provision of caring and individualized attention to passengers. Training
programs that emphasize customer service skills can enhance crew performance, leading to
improved interactions with passengers. Additionally, airlines that actively seek to understand
and cater to the emotional needs of their passengers—through personalized communication
or attentiveness—often see increased loyalty and positive feedback.

High service quality is shown to mitigate negative experiences, as passengers who perceive
high-quality service are more likely to remain loyal even in the face of service failures. For
instance, airlines that maintain clean and well-maintained aircraft while ensuring prompt and
courteous service tend to receive higher satisfaction ratings from their passengers. This
suggests that service recovery strategies, such as compensatory offers or sincere apologies
during disruptions, can also play a crucial role in retaining customer loyalty.

Passenger Experience:
The passenger experience extends beyond the flight itself and encompasses pre-flight, in-
flight, and post-flight interactions. The overall travel experience is integral to passenger
satisfaction. Managing touchpoints across the entire journey is crucial, as airlines must ensure
seamless transitions from booking to check-in, boarding, and in-flight service.

A holistic approach to service experience management is essential for airlines to create

memorable experiences that resonate with passengers. This perspective aligns with the
increasing emphasis on experiential marketing in the airline industry, where the goal is to
establish a positive emotional connection with passengers. For example, airlines that provide
engaging and user-friendly booking platforms, as well as personalized communication during
the travel journey, can significantly enhance the overall passenger experience.

Additionally, the impact of airport services on the overall travel experience cannot be
overlooked. Factors such as airport amenities, security procedures, and terminal layout
significantly affect passenger satisfaction before boarding. Efficient check-in processes, short
security wait times, and comfortable lounge areas can greatly enhance the pre-flight
experience, setting a positive tone for the journey ahead. This underscores the importance of
a comprehensive approach to service quality that considers all aspects of the passenger

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journey, including collaborations with airports to improve the overall environment and
services.

2.2 Passenger Satisfaction in the Airline Industry

Airline passenger satisfaction is a multi-dimensional concept influenced by various service
related attributes. Several studies have identified key factors affecting customer experience,
including:
• Flight Punctuality: Delays and cancellations significantly impact passenger
satisfaction. Studies indicate that on-time performance is one of the strongest
predictors of customer loyalty.
• Inflight Services: Comfort, food quality, WiFi availability, and entertainment options
play a crucial role in passenger satisfaction.
• Check-in & Boarding Process: A smooth and hassle-free check-in and boarding
experience enhances passenger satisfaction.
• Baggage Handling: Mishandled baggage is a leading cause of customer
dissatisfaction. Airlines with efficient baggage handling systems tend to have higher
satisfaction scores.
• Customer Service & Complaint Resolution: Airlines that promptly address customer
complaints and provide proactive solutions improve their overall service ratings.
Research shows that airlines implementing data-driven strategies for operational
improvements experience higher customer retention and brand loyalty.

2.3 Role of Data Analytics in Passenger Satisfaction Prediction

The use of data analytics and ML in the airline industry has revolutionized how customer
satisfaction is measured and predicted. Researchers have developed various predictive
models using machine learning to classify passengers as satisfied or dissatisfied based on
different attributes.
2.3.1 Machine Learning Models for Customer Satisfaction Prediction
Several studies have explored the effectiveness of machine learning algorithms in predicting
passenger satisfaction. Commonly used models include:
• Logistic Regression: A widely used classification model that helps in binary prediction
(satisfied vs. dissatisfied).

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 • Decision Trees & Random Forest: These models identify key factors contributing to
satisfaction by analyzing feature importance.
• Support Vector Machines (SVMs): Effective in handling complex relationships
between satisfaction levels and service attributes.
• Neural Networks: Deep learning techniques that provide high prediction accuracy but
require large datasets for training.
Studies indicate that ensemble methods like Random Forest and Gradient Boosting tend to
outperform traditional models in terms of accuracy and reliability in predicting airline
passenger satisfaction.
2.3.2 Data Sources and Feature Engineering
To develop accurate satisfaction prediction models, researchers use various data sources,
including:
• Passenger surveys (feedback forms, online reviews, Net Promoter Score)
• Operational airline data (flight delays, seating arrangements, service usage)
• Social media sentiment analysis (Twitter, TripAdvisor, airline forums)
Feature engineering plays a crucial role in improving model performance. Studies suggest
that combining structured survey data with unstructured sentiment analysis can enhance
predictive accuracy.

2.4 Challenges in Predicting Passenger Satisfaction

Despite advancements in machine learning and predictive modelling, several challenges
remain in accurately predicting passenger satisfaction:
1. Data Quality Issues – Missing or biased data can impact model accuracy.
2. Dynamic Customer Preferences – Passenger expectations change over time, making it
difficult to develop static prediction models.
3. External Factors – Weather conditions, airport congestion, and economic factors
influence satisfaction but are difficult to quantify.
4. Interpretability of ML Models – Black-box models like deep learning provide high
accuracy but lack explainability, making it difficult for airlines to derive actionable
insights.

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 CHAPTER 3 – RESEARCH METHODOLOGY

This chapter outlines the research methodology adopted for predicting airline passenger
satisfaction using machine learning techniques. It includes the research method, sampling
technique, data collection process, types of data used, and methods of data collection. The
approach ensures that the study is based on structured data analysis, enabling accurate and
insightful predictions of passenger satisfaction levels.

3.1 Research Method

This study employs a quantitative research approach through secondary data analysis,
leveraging machine learning techniques to predict airline passenger satisfaction. The research
method is structured into data collection, data preprocessing, model selection, training, and
evaluation to develop an accurate and reliable predictive model.

The research will utilize a quantitative approach, employing statistical analysis and machine
learning techniques to predict passenger satisfaction. The primary methods include:
- Descriptive Statistics: To summarize the data and understand key trends.
- Inferential Statistics: To draw conclusions about the population from the sample data. -
Predictive Modelling: Using machine learning algorithms to predict satisfaction levels based
on input features.

3.1.1 Research Approach

The research follows a data-driven approach, where insights are extracted from structured
datasets rather than primary surveys or interviews. The machine learning methodology
involves supervised learning, where labelled data (satisfied/dissatisfied passengers) is used to
train predictive models. This ensures that the findings are objective, reproducible, and
scalable for real-world applications in the airline industry.
3.1.2 Rationale for Using Secondary Data
Secondary data analysis is selected due to the following advantages:

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 • Availability of Large-Scale Data: Airlines collect extensive customer feedback
through surveys, which is publicly available in structured datasets.
• Cost and Time Efficiency: Using pre-existing data eliminates the need for new data
collection, reducing research costs and duration.
• Enhanced Model Performance: Larger datasets improve machine learning model
generalizability, leading to more accurate predictions.
• Comparability with Existing Studies: Using standard datasets enables comparisons
with prior research in passenger satisfaction analytics.
3.1.3 Research Design
The research follows a structured process that includes:
1. Data Collection: Extracting airline customer satisfaction data from Kaggle and other
open-source repositories.
2. Data Preprocessing: Cleaning and transforming data to ensure consistency and
reliability.
3. Feature Selection & Engineering: Identifying and optimizing key predictors of
passenger satisfaction.
4. Model Selection & Training: Applying classification algorithms to develop a
predictive model.
5. Performance Evaluation: Assessing model accuracy using various statistical metrics.
6. Interpretation of Results: Extracting actionable insights to improve airline customer
experience.
3.1.4 Supervised Machine Learning Approach
This study applies classification-based supervised learning models, where input variables
(passenger demographics, flight details, service ratings) are mapped to an output variable
(satisfaction level). The process includes:
• Training Phase: The model learns patterns from labeled data (80% of the dataset).
• Testing Phase: The model predicts satisfaction levels on unseen data (20% of the
dataset).
• Evaluation Phase: Performance metrics (accuracy, precision, recall, F1-score) are
calculated to validate model effectiveness.
3.1.5 Ethical Considerations in Secondary Data Usage
While secondary data analysis eliminates ethical concerns related to direct data collection,
ethical principles such as data privacy, bias mitigation, and transparency are upheld:

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 • Privacy Protection: The dataset does not contain personally identifiable information
(PII).
• Bias Reduction: Stratified sampling ensures fair representation of all passenger types.
• Transparency: The study adheres to data usage policies of Kaggle and academic
research standards.
3.1.6 Justification of the Methodology
This research method is selected because it aligns with the objectives of predicting passenger
satisfaction trends, improving airline service strategies, and leveraging machine learning for
business insights. The structured application of data science principles ensures that the study
remains rigorous, replicable, and valuable for real-world airline operations.
This detailed research method section provides a strong foundation for the study’s
methodology, ensuring that the analysis is systematic, data-driven, and ethically sound.

3.2 Sampling
To ensure the reliability and generalizability of the model, a stratified sampling technique is
employed. This technique is chosen because passenger satisfaction is influenced by various
factors, and an uneven distribution of satisfied and dissatisfied passengers could lead to
biased predictions.
A stratified random sampling method will be employed to ensure representation across
different passenger demographics and travel classes. This approach helps in capturing the
diversity of experiences among passengers, including frequent flyers and first-time travellers.
The sample size will be determined based on the total population of airline passengers to
ensure statistical significance.

3.2.1 Sampling Strategy

• Stratified Sampling: The dataset is divided into groups based on satisfaction levels
(satisfied and dissatisfied passengers). This ensures that both categories are
wellrepresented in the training and testing phases.
• Train-Test Split: The dataset is randomly split into 80% training data and 20% testing
data to allow the model to learn from historical passenger experiences while
maintaining an independent test set for evaluation.
• Cross-Validation: A 5-fold cross-validation approach is used to prevent overfitting and
improve model robustness. In this approach, the dataset is split into five subsets,

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 where the model is trained on four subsets and tested on the remaining subset. This
process is repeated five times to ensure a more generalized model.
The stratified sampling technique ensures that the model is trained on a dataset that
accurately reflects the real-world distribution of airline passengers.

3.3 Data Collection

The study relies on secondary data sources, specifically publicly available datasets from
Kaggle and similar repositories. The dataset consists of airline passenger feedback collected
from various sources, including customer surveys and operational airline data.

Data will be collected from multiple sources:

- Surveys: Distributed to passengers after their flights to gather direct feedback on their
experiences.
- Airline Records: Internal data such as flight schedules, delays, and operational
statistics. - Online Reviews: Analyzing reviews from platforms like TripAdvisor and airline
websites to capture public sentiment.

The data collection process follows these key steps:

1. Identification of a relevant dataset: The study selects a dataset that includes detailed
airline passenger satisfaction ratings, ensuring a comprehensive view of customer
experiences.
2. Dataset Evaluation: The dataset is reviewed for completeness, accuracy, and relevance
to ensure that it meets the requirements for machine learning modelling.
3. Data Download and Preprocessing: The dataset is extracted, formatted, and cleaned to
ensure it is suitable for analysis and machine learning applications.
The collected data provides a rich source of structured information, allowing for quantitative
analysis of passenger satisfaction levels.

3.4 Types of Data

The dataset used in this study consists of both quantitative and qualitative data. The
combination of numerical and categorical attributes enables a more comprehensive
understanding of passenger satisfaction factors.

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The project will involve various types of data:
- Quantitative Data: Numerical ratings of satisfaction, flight durations, and
demographic details (age, income).
- Categorical Data: Gender, travel class, and feedback categories (e.g., positive, neutral,
negative).
- Text Data: Open-ended responses from surveys and online reviews, which will require
text analysis techniques.
3.4.1 Quantitative Data (Numerical Variables)
This includes numerical variables that can be measured and analysed statistically:
• Passenger Age: The age of the passenger, which may impact satisfaction levels.
• Flight Distance: The distance of the flight, influencing overall experience and
comfort.
• Departure Delay in Minutes: The time delay before take-off.
• Arrival Delay in Minutes: The delay in landing, affecting passenger convenience.

3.4.2 Qualitative Data (Categorical Variables)

This includes categorical attributes representing passenger characteristics and feedback:
• Gender: Male/Female classification to study differences in satisfaction trends.
• Customer Type: Whether the passenger is a loyal customer or a first-time flyer.
• Class of Travel: The class in which the passenger is flying (Business, Economy,
Economy Plus).
• Type of Travel: Business travel or personal travel.
• Satisfaction Ratings: Categorical ratings of various services such as inflight Wi-Fi,
seat comfort, baggage handling, food & beverage quality, and customer service.
The combination of quantitative and qualitative data ensures a detailed and accurate analysis
of passenger satisfaction, capturing multiple dimensions of customer experience.

3.5 Methods of Data Collection

The dataset is obtained from publicly available sources such as Kaggle, a well-known
platform providing structured datasets for machine learning applications. The process of data
collection follows a structured approach to ensure reliability and consistency.

3.5.1 Data Collection Steps

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 • Keyword Search: Relevant datasets are identified using keywords like "airline
passenger satisfaction dataset," "airline customer reviews," and "passenger feedback
data."
• Dataset Evaluation: The selected dataset is examined for accuracy, completeness, and
coverage of key passenger satisfaction factors.
• Download and Integration: The dataset is downloaded in CSV format and integrated
into Python, SQL, Power BI, and Tableau for further analysis.
3.5.2 Preprocessing of Collected Data
Once the dataset is collected, preprocessing is performed to prepare it for machine learning
analysis. This includes:
• Handling Missing Data: Filling in missing values using mean/mode imputation
techniques.
• Encoding Categorical Variables: Converting categorical variables into numerical
format using one-hot encoding and label encoding.
• Feature Engineering: Creating new variables such as Total Delay Time (sum of
departure and arrival delays) to enhance predictive power.
• Data Normalization: Scaling numerical features using Min-Max Scaling or
Standardization to improve model performance.
These preprocessing techniques ensure that the dataset is clean, structured, and suitable for
machine learning applications.

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CHAPTER 4 – DATA ANALYSIS & INTERPRETATION

• Pandas: A powerful Python library for data manipulation and analysis. It provides
efficient data structures such as Data Frames and Series, which allow users to handle
structured data conveniently. Pandas offers various built-in functions for data
cleaning, transformation, aggregation, and visualization.
• Matplotlib.pyplot: Matplotlib's pyplot module is a simple and powerful tool for
creating graphs and charts in Python. It provides an easy way to visualize data by
drawing line graphs, bar charts, scatter plots, and more. Like MATLAB, it allows
users to customize their plots by adding titles, labels, legends, and colours to make the
data more understandable.
• Seaborn: A Python visualization library built on Matplotlib, designed to create
statistical and aesthetically appealing graphics. It provides functions for scatter plots,
histograms, box plots, violin plots, and heatmaps, often with built-in statistical
transformations for data analysis.
• NumPy: A fundamental package for scientific computing in Python, designed to
handle large multidimensional arrays and matrices efficiently. It includes a wide range
of mathematical functions for linear algebra, Fourier transforms, and random number
generation.
• %matplotlib inline: A Jupiter Notebook magic command that ensures Matplotlib
plots are displayed within the notebook instead of opening a separate window. It is
commonly used for inline visualization while working in interactive environments like
Jupiter.
• sns.set(rc={'figure.figsize':(15, 8)}, font_scale=1.3): rc={'figure.figsize':(15, 8)} sets
the default plot size to 15 inches wide and 8 inches tall and font_scale=1.3 Adjusts the
font size for labels, titles, and ticks to make them more readable.

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Dataset-https://www.kaggle.com/datasets/teejmahal20/airline-passenger-satisfaction

1.Are Airline Passengers Satisfied ?

INPUT:

OUTPUT:

• df: This is the DataFrame that contains the dataset.

• Satisfaction: This selects the column named "satisfaction", which likely stores
whether a passenger is satisfied or dissatisfied.
• .value_counts(): This function counts the occurrences of each unique value in the
selected column.

INPUT:

OUTPUT:

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 • plt.pie: It is used for creating a pie chart, which are circular statistical graphics that
divide a dataset into proportional slices.

• df['satisfaction'].value_counts(): This function Counts how many passengers are

Satisfied vs Dissatisfied or Neutral.
• labels=['Neutral or Dissatisfied', 'Satisfied']: It defines the labels for two categories
in the pie chart. The first segment represents Neutral or Dissatisfied passengers, and
the second represents Satisfied passengers. These labels help to identify what each
portion of the pie chart represents.
• Shadow=True: It Adds a shadow effect beneath the pie chart, giving it a 3D-like
appearance making the visualization more visually appealing and easier to distinguish.
• autopct='%.2f%%',: This adds percentage values to each section of the pie chart,
helping us understand the exact proportions of satisfied vs. dissatisfied passengers.
• colors=['r','b'],: These colors define how each category will be represented in the
chart like r(Red) for Neutral/Dissatisfied passengers and b(blue) for satisfied
passengers. Using distinct colours makes the chart easier to interpret and
understandable.
• explode=[0.1,0],: It Separates the first slice (Neutral or Dissatisfied) slightly outward
for emphasis and 0.1 moves the first slice outward, while 0 keeps the second slice in
place.
• startangle=90): Starts the pie chart rotated at 90 degrees to make it look visually
balanced.
• plt.title('Are Airline Passengers Satisfied ?', size=20): They Add a title to the chart
with larger font size (20).
• plt.axis('equal'): Ensures the pie chart is a perfect circle rather than an oval.

2. Does Passenger Gender Affect His Satisfaction?

INPUT:

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OUTPUT:

• sns.countplot: It Creates a bar chart that counts the number of occurrences for each
category in the specified column.

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 • x=df['Gender']: The x-axis represents the Gender column, meaning the bars will
represent counts of male and female passengers.
• order=df['Gender'].value_counts(): They Counts how many times each gender
appears in the dataset.
• .Index : Retrieves the ordered categories (Male and Female) based on their
frequency This ensures that the bars are ordered correctly (e.g., the most common
gender appears first).
• hue=df['satisfaction']): The hue argument groups the bars by the satisfaction levels.
this means each gender will have two bars (one for satisfied passengers and one for
dissatisfied/neutral passengers)This helps to compare the satisfaction levels between
male and female
• plt.title('Satisfaction Degree and Gender', size=20): It Sets the title of the chart to
"Satisfaction Degree and Gender". The size=20 makes the title large and easy to
read. • plt.show(): This displays the generated count plot

Input:

Output:

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 • sns.heatmap(): Creates a heatmap based on a dataset.
• Gender_Stafication Percentage: This is the dataset used to generate the heatmap. It
likely contains percentages of satisfied vs. dissatisfied passengers for each gender.
• linewidths=1.5: Sets the thickness of the grid lines separating the cells for better
clarity.
• annot=True: Displays the actual percentage values inside each cell, rather than just
using colors.
• fmt='.2f': Formats the values to two decimal places (e.g., 45.23% instead of
45. 234567).
• plt.title('Stafication Percentage in Each Gender', size=20): Sets the title of the
heatmap to Satisfaction Percentage in Each Gender.

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3.How Passenger Class Affect Satisfaction Degree ?
INPUT:

OUTPUT:

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 • sns.countplot(): Generates a bar chart that displays the count of occurrences for each
category in the specified column.
• x=df['Class']: The x-axis represents different flight classes (e.g., Economy,
Business, First Class). Each bar represents the number of passengers in that class.
• hue=df['satisfaction']: This groups the bars by satisfaction levels, meaning each
flight class will have separate bars for satisfied and dissatisfied/neutral passengers.
• plt.title('Satisfaction Degree and Flight Class', size=20): Sets the title of the chart
to satisfaction degree and flight class. The size 20 increase the font size for better
readability
• plt.show(): Display the generated count plot

Input:
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Output:

• sns.heatmap(): Creates a heatmap based on a dataset.

• Flight_class_Stafication Percentage: This is the dataset used to generate the
heatmap. It likely contains percentages of satisfied vs. dissatisfied passengers for
each gender.
• linewidths=1.5: Sets the thickness of the grid lines separating the cells for better
clarity.
• Annot=True: Displays the actual percentage values inside each cell, rather than just
using colors.
• Fmt='.2f': Formats the values to two decimal places (e.g., 45.23% instead of
45. 234567).
• plt.title('Stafication Percentage in Each Flight Class', size=20): Sets the title of
the heatmap to "Satisfaction Percentage in Each Flight Class". The size=20 increases
the font size for better readability.
• plt.show(): Display the heat map

4.What is Age Average for Satisfied Passenger and Dissatisfied Passenger?

INPUT:

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OUTPUT:

• sns.boxplot(): This function creates a box plot, which is used to visualize the
distribution of a numerical variable and detect outliers.
• y = df['Age']: Specifies that the "Age" column from the dataframe df should be
plotted along the Y-axis.

input:

output:

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 sns.barplot(x=df['satisfaction'], y=df['Age'], estimator=np.mean):

• This creates a bar plot where:

➢ X-axis (x=df['satisfaction']): Represents passenger satisfaction

categories (e.g., Satisfied and Neutral or Dissatisfied).
➢ Y-axis (y=df['Age']): Represents the passengers' ages.
➢ estimator=np.mean: The function calculates the average (mean) age
for each satisfaction group.

• plt.title('Age Average and Satisfaction Degree', size=20): They Set the title of the
plot to "Age Average and Satisfaction Degree" with a font size of 20. • plt.show():
Display the plot

5.Does Delay Time in Departure or Arrival Affect on Passenger Satisfaction ?

Arrival Delay INPUT:

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OUTPUT:

Departure Delay
• sns.barplot(x=df['satisfaction'], y=df['Arrival Delay in Minutes'],
estimator=np.mean)

Creates a bar plot where:

➢ X-axis (x=df['satisfaction']): Represents passenger satisfaction levels (e.g.,
Satisfied vs. Neutral or Dissatisfied).
➢ Y-axis (y=df['Arrival Delay in Minutes']): Represents the average arrival
delay for each satisfaction group.
➢ estimator=np.mean: Calculates the average (mean) arrival delay for each
satisfaction category.

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 • plt.title('Arrival Delay and Satisfaction Degree', size=20): Sets the title of the plot
to "Arrival Delay and Satisfaction Degree" with a font size of 20.
• plt.show(): Displays the plot.

INPUT:

OUTPUT:

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 •
sns.barplot(x=df['satisfaction'],y=df['Departur Delay in Minutes'], estimator=np.mean)

Creates a bar plot where:

➢ X-axis (x=df['satisfaction']): Represents passenger satisfaction levels (e.g.,

Satisfied vs. Neutral or Dissatisfied).
➢ Y-axis (y=df['Departure Delay in Minutes']): Represents the average
Departure delay for each satisfaction group.
➢ estimator=np.mean: Calculates the average (mean) arrival delay for each
satisfaction category.

• plt.title('Arrival Delay and Satisfaction Degree', size=20): Sets the title of the plot
to "Arrival Delay and Satisfaction Degree" with a font size of 20.
• plt.show(): Displays the plot.

6.Is Relation between inflight Wifi and Passenger Satisfaction ?

Input:

Output:

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 •
satisfaction: Indicates whether the passenger is satisfied or not.
• Inflight wifi service: Represents the passenger’s rating of the in-flight WiFi service.
• map(rating_dict): This function replaces the numeric ratings in the Inflight wifi
service column with values from a predefined dictionary rating_dict.
• sns.countplot() is used to create a bar plot that counts the number of occurrences of
each satisfaction level.
• x=satisfaction_and_wifi['satisfaction']: The x-axis represents passenger
satisfaction levels.
• hue=satisfaction_and_wifi['Inflight wifi service']: The bars are divided into
different colors based on WiFi ratings (e.g., "Very Poor", "Good", "Excellent").
• palette='Paired_r': Defines the color scheme for the bars using the Paired_r palette
(a reversed version of the Seaborn "Paired" color palette).
• plt.title('InFlight Wifi vs Satisfaction Degree', size=18): This adds title to the plot
and size 18 make the text better and readable.

7.Is Increasing Flight Distance Affecting Passenger Satisfaction ?

Input:

Output:

• df.groupby('satisfaction'): This groups the dataset by the 'satisfaction' column,

meaning all rows with the same satisfaction level are grouped together.
• .describe(): The describe() function calculates key summary statistics for numerical
columns within each satisfaction group.

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 •
• ['Flight Distance']: This extracts only the summary statistics for the 'Flight
Distance' column, ignoring other numerical columns.
.T: The. T transposes the table, flipping rows into columns and columns into rows for
better readability.
Input:

Output:

• sns.boxplot(): sns.boxplot() is used to create a box plot, which helps visualize data
distribution, variability, and outliers.
• x=df['satisfaction']: This groups the flight distance data based on passenger
satisfaction levels. Each category will have its own box plot.
• y=df['Flight Distance']: The vertical axis represents the distribution of flight
distances for each satisfaction level. This helps us see if satisfaction levels are
affected by flight distance.

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 •

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Input:

• sns.boxenplot(): sns.boxenplot() is similar to a box plot, but it is designed for better

visualization of large datasets with many outliers.
• x=df['satisfaction']: The x-axis represents passenger satisfaction levels, The flight distance
data will be grouped by satisfaction level.
• y=df['Flight Distance']: The y-axis represents the distribution of flight distances for each
satisfaction group.

8.How Seat Comfort Affect Passenger Satisfaction ?

Input:

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Output:

• satisfaction: Passenger satisfaction level (Satisfied or Neutral/Dissatisfied).

• Seat comfort: The passenger's rating of seat comfort (originally in numerical form).
• .map(rating_dict): replaces the numeric ratings in the Seat comfort column with
descriptive labels from a predefined dictionary called rating_dict.

• sns.countplot(): creates a bar chart that shows the number of passengers for each
satisfaction level.
• x=satisfaction_and_seat_comfort['satisfaction']: The x-axis represents passenger
satisfaction levels (e.g., "Satisfied" and "Neutral or dissatisfied").
• hue=satisfaction_and_seat_comfort['Seat comfort']: The bars are divided by seat comfort
ratings (e.g., "Very Poor", "Good", "Excellent").
• palette='Paired_r': Uses a reversed "Paired" colour scheme for better visualization.
• plt.title('Seat Comfort vs Satisfaction Degree', size=18): This adds a title to the plot.
size=18 makes the title larger and easier to read.

9. What is Cleanliness Rate that Satisfy Passenger ?

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output:

Output:

10. What Relation Between Customer Type and Satisfaction ?

Input:

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Output:

• stastfied = df[df['satisfaction'] == 'Satisfied']:

➢ df['satisfaction'] == 'Satisfied' creates a boolean mask (True for satisfied

passengers, False for others).
➢ df[...] applies this mask to filter the DataFrame, keeping only satisfied passengers.

• neutral_dissatisfied = df[df['satisfaction'] == 'Neutral or Dissatisfied']:

➢ This creates another DataFrame called neutral dissatisfied, containing only

passengers who are "Neutral or Dissatisfied".
➢ The process is the same as before, but the filter condition is 'Neutral or Dissatisfied'
instead of 'Satisfied'.

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 • Customer types: Retrieves unique customer types (e.g., "Loyal Customer", "Disloyal
Customer") from the 'Customer Type' column. And converts them into a list for use labels on
the x axis.
• fig, ax = plt.subplots(): Creates a figure (fig) and an axes object (ax) for the bar plot. •
figsize=(15,8): Sets the plot size to 15 inches wide and 8 inches tall

• ax.bar(labels, stastfied['Customer Type'].value_counts(), label='stastfied',

facecolor='b', edgecolor='k'):

➢ ax.bar() → Creates a bar chart.

➢ labels → Sets the x-axis categories (customer types).
➢ stastfied['Customer Type'].value_counts() → Counts the number of satisfied
passengers in each customer type.
➢ facecolor='b' → Colours the bars blue (for satisfied passengers).
➢ edgecolor='k' → Adds black edges to the bars for better visibility. ➢
label='stastfied' → Assigns a label for the legend.

• ax.bar(labels, neutral_dissatisfied['Customer Type'].value_counts(),

bottom=stastfied['Customer Type'].value_counts(), label="Neutral or Dissatisfied",
facecolor='r', edgecolor='k'):

➢ bottom=stastfied['Customer Type'].value_counts()→ Stacks these bars

on top of the satisfied bars.
➢ facecolor=r → Colours these bars red (for neutral/dissatisfied passengers).
➢ label="Neutral or Dissatisfied" → Assigns a label for the legend.

Input:

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Output:

• sns.countplot(): Creates a bar chart that counts the number of passengers for each customer
type.

• x=df['Customer Type']: The x-axis represents different customer types (e.g., "Loyal
Customer", "Disloyal Customer").

• hue=df['satisfaction']: Splits each customer type into two groups based on satisfaction like
satisfied and neutral or dissastisfied

• palette='mako': Uses the "mako" color scheme (a blue-green gradient) to make the plot
visually appealing.

• plt.title('Satisfaction Degree vs Customer Type', size=18): They add a title to the graph
and size 18 makes the title larger and easier to read.
• Plt.show(): It ensures that the plot is displayed
11. What Food and Drink Quality that Satisfy Passenger ?

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Input:

Output:

Creates a new DataFrame (satisfaction_and_food) containing only two columns:

➢ Satisfaction → Indicates whether a passenger is Satisfied or Neutral/Dissatisfied.

➢ Food and drink → Represents passenger ratings for the airline's food and drink service.

• map(rating_dict): Replaces each text-based rating with its corresponding number

(e.g., "Very Poor" → 1, "Excellent" → 5).

• sns.countplot(): Creates a bar chart that counts the number of passengers for each customer
type.

• x=satisfaction_and_food['satisfaction']: The x-axis represents passenger satisfaction

categories:
➢ "Satisfied"
➢ "Neutral or Dissatisfied"

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 • hue=satisfaction_and_food['Food and drink']: Divides each satisfaction group into
subcategories based on food and drink ratings.Each bar is split into multiple segments,
representing different food ratings (e.g., 1 to 5).
• palette='Paired_r' → Uses the Paired_r color palette to differentiate food and drink
ratings visually.
• plt.title('Food and Drink Quality vs Satisfaction Degree', size=18): Adds a title to
the graph, making it clear that the plot is about food quality and satisfaction. size=18
makes the title larger and easier to read.

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 CHAPTER 5 – FINDINGS, CONCLUSIONS &
RECOMMENDATIONS
5.1 Findings
1. Are Airline Passengers Satisfied?
• 56.5% of passengers are either neutral or dissatisfied, while only 43.5% are satisfied.
• A higher proportion of dissatisfied passengers are found in economy class, while
business class passengers tend to be more satisfied.
• The primary dissatisfaction factors include seat comfort, inflight services, and flight
delays.
• Punctuality, inflight entertainment, and service quality are major influences on
satisfaction levels.
• Business-class travelers tend to rate their experiences much higher compared to
economy-class passengers.

2. Does Passenger Gender Affect Satisfaction?

• The percentage of neutral or dissatisfied passengers is similar between genders:
o 57.11% of female passengers are neutral or dissatisfied.
o 55.97% of male passengers are neutral or dissatisfied.
• This indicates that gender does not play a major role in passenger satisfaction.
• Instead, factors such as travel class, cleanliness, seat comfort, inflight entertainment,
and punctuality have a larger impact on overall satisfaction.
• Both male and female passengers tend to rate inflight Wi-Fi and entertainment as
areas needing improvement.

3. How Does Passenger Class Affect Satisfaction?

• Business class passengers have the highest satisfaction rates (69.45%).
• Only 18.76% of economy class passengers reported being satisfied.
• Economy Plus passengers have slightly better satisfaction levels at 24.63%, but still
significantly lower than business class.

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 The key reasons for dissatisfaction among economy-class travelers include:
o Uncomfortable seating with limited legroom. o Lower meal quality.
o Lack of exclusive services compared to premium travelers.
• Business-class passengers benefit from better seating, superior meal options, and
higher service standards, leading to higher satisfaction levels.

4. What Is the Average Age of Satisfied vs. Dissatisfied Passengers?

• The average age of satisfied passengers is approximately 42 years.
• Passengers below the age of 30 are more likely to be dissatisfied.
• Younger travelers tend to expect better entertainment, faster Wi-Fi, and superior
service.
• Older passengers are generally more patient with minor inconveniences and are
satisfied with cleanliness, meal options, and seating comfort.
• Younger travelers frequently complain about Wi-Fi speed, limited seat space, and the
quality of inflight entertainment.

5. Does Delay Time in Departure or Arrival Affect Satisfaction?

• Flight delays negatively impact passenger satisfaction.
• The longer the delay, the higher the likelihood of dissatisfaction.
• Passengers on delayed flights rated their overall experience lower, regardless of their
travel class.
• Business-class passengers were more forgiving of short delays due to better amenities
in airport lounges.
• Economy-class passengers were more likely to express frustration with delays,
especially if there was a lack of communication from the airline.
• Delays longer than 30 minutes significantly increased dissatisfaction levels.

6. Is There a Relationship Between Inflight Wi-Fi and Passenger Satisfaction?

• Passengers who rated inflight Wi-Fi poorly were mostly dissatisfied.
• The most common complaints about inflight Wi-Fi included:
o Slow internet speeds. o Frequent connectivity drops.
•

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 o High costs for premium internet access.
Younger passengers (below 35) were the most affected by Wi-Fi issues, as they rely
more on internet access for entertainment and work.
• Many passengers feel that Wi-Fi should be a standard service rather than a premium
feature.

7. Does Increasing Flight Distance Affect Passenger Satisfaction?

• Satisfaction slightly decreases as flight distance increases.
• Passengers on longer flights demand better entertainment, food quality, and more
comfortable seating.
• Economy-class travelers on long-haul flights reported higher dissatisfaction, mainly
due to:
o Uncomfortable seating for extended periods. o Limited food and drink
service.
o Poor inflight entertainment selection.
• Business-class passengers, on the other hand, were generally satisfied with their
longhaul experience due to better accommodations.

8. How Does Seat Comfort Affect Passenger Satisfaction?

• Seat comfort is one of the strongest factors influencing passenger satisfaction.
• Passengers who rated seat comfort poorly were significantly more likely to be
dissatisfied.
• Common complaints about seats in economy class:
o Limited legroom.
o Hard or poorly cushioned seats.
o Limited recline options.
• Longer flights tend to amplify discomfort, making seat quality even more critical.
• Business class passengers generally rated seat comfort as satisfactory or excellent,
indicating that premium seating options improve passenger experience.

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9. What Cleanliness Level Satisfies Passengers?
• Passengers who rated cleanliness as "Excellent (5)" or "Very Good (4)" were mostly
satisfied.
• Dissatisfied passengers often rated cleanliness as "Good (3)" or lower.
Cleanliness is particularly important in areas such as:
o Tray tables, seats, and armrests. o Restrooms and common areas.

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 o Cabin air quality and odor control.
• Passengers expect high cleanliness standards, particularly in economy class, where
they spend more time in close quarters.

10. What Is the Relationship Between Customer Type and Satisfaction?

• Loyal customers tend to have higher satisfaction levels than one-time travelers.
• Passengers who frequently travel with the airline appreciate loyalty rewards and
exclusive perks.
• First-time flyers or occasional travelers reported lower satisfaction, as they may not be
as familiar with the airline’s service expectations.
• Common benefits that increase satisfaction among loyal customers:
o Priority boarding. o Seat upgrades.
o Better baggage handling and faster check-in.
• Occasional travelers were more likely to be dissatisfied due to lack of special
treatment or loyalty rewards.

11. What Food and Drink Quality Satisfies Passengers?

• Food and drink quality is a critical factor in passenger satisfaction.
• Passengers who rated food and drinks poorly were more likely to be dissatisfied.
• Common complaints about food and beverages:
o Limited menu options, especially for special diets (vegetarian, vegan,
glutenfree).
o Lack of fresh ingredients in economy-class meals.
o Small portion sizes.
• Business-class passengers were generally more satisfied with food quality, as they had
access to a broader selection of meals and beverages.
• Economy-class passengers reported a need for better variety and higher-quality meal
options.

5.2 Conclusion
1. Are Airline Passengers Satisfied?
Conclusion: A significant portion of passengers are not fully satisfied with their flying
experience, with more than half being neutral or dissatisfied. Passenger satisfaction is

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strongly influenced by travel class, service quality, and punctuality. Airlines need to address
dissatisfaction factors in economy class to improve overall ratings.

2. Does Passenger Gender Affect Satisfaction?

Conclusion: Gender does not play a major role in passenger satisfaction. Both male and
female passengers show similar satisfaction levels. Instead of gender, other factors like travel
class, seat comfort, and service quality have a more significant impact on the overall
experience.

3. How Does Passenger Class Affect Satisfaction?

Conclusion: Passenger class has a major impact on satisfaction. Business-class travellers
are significantly more satisfied due to better seating, premium services, and higher meal
quality. Economy-class passengers, on the other hand, experience lower satisfaction due to
discomfort, limited amenities, and fewer perks. Airlines should focus on enhancing
economyclass experience to improve overall customer satisfaction.

4. What Is the Average Age of Satisfied vs. Dissatisfied Passengers?

Conclusion: Older passengers tend to be more satisfied, while younger travellers express
more dissatisfaction. Younger passengers expect better inflight entertainment, stronger Wi-Fi,
and improved seating comfort. Older passengers are more tolerant of minor inconveniences
and are satisfied with cleanliness and basic services.

5. Does Delay Time in Departure or Arrival Affect Satisfaction?

Conclusion: Flight delays have a significant negative impact on passenger satisfaction. The
longer the delay, the greater the dissatisfaction. Business-class travellers are slightly more
forgiving due to access to lounges, while economy-class passengers are highly sensitive to
delays. Reducing flight delays and improving communication during disruptions can help
maintain satisfaction levels.

6. Is There a Relationship Between Inflight Wi-Fi and Passenger Satisfaction?

Conclusion: Inflight Wi-Fi quality has a strong correlation with satisfaction levels.
Passengers, especially younger travelers, expect reliable and fast internet access. Poor Wi-Fi

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connectivity is a frequent complaint and significantly contributes to dissatisfaction. Airlines
should invest in better inflight Wi-Fi services to meet passenger expectations.

7. Does Increasing Flight Distance Affect Passenger Satisfaction?

Conclusion: Longer flights tend to lower satisfaction levels, especially in economy class. As
flight duration increases, seat comfort, meal quality, and entertainment options become more
important. Airlines should focus on enhancing long-haul travel experiences to maintain high
satisfaction levels.

8. How Does Seat Comfort Affect Passenger Satisfaction?

Conclusion: Seat comfort is a major determinant of passenger satisfaction. Passengers who
experience discomfort due to limited legroom, hard seats, and restricted recline tend to rate
their experience poorly. Business-class passengers have higher satisfaction due to superior
seating, while economy-class travelers frequently express discomfort. Airlines should
prioritize ergonomic seating improvements, especially for economy-class customers.

9. What Cleanliness Level Satisfies Passengers?

Conclusion: High cleanliness standards lead to greater satisfaction. Passengers who
perceive the aircraft as well-maintained and hygienic are more likely to rate their experience
positively. Cleanliness is especially important in tray tables, seating areas, restrooms, and
cabin air quality. Airlines must ensure rigorous cleaning protocols to maintain high standards
and improve passenger trust.

10. What Is the Relationship Between Customer Type and Satisfaction?

Conclusion: Frequent flyers and loyal customers tend to have higher satisfaction levels
compared to occasional travelers. Repeat customers appreciate loyalty perks such as priority
boarding, seat upgrades, and better service, leading to a more positive travel experience.
Firsttime or occasional travelers may have unrealistic expectations, leading to lower
satisfaction levels.

11. What Food and Drink Quality Satisfies Passengers?

Conclusion: Food and drink quality has a strong influence on passenger satisfaction.
Business-class passengers are generally satisfied with meal options, while economy-class

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travelers frequently complain about limited menu choices, lack of fresh ingredients, and
small portions. Improving food variety and quality can enhance overall satisfaction.

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5.3 Recommendations
1. Are Airline Passengers Satisfied?
Recommendation: Airlines should focus on enhancing economy-class passenger
experience by improving seat comfort, inflight services, and reducing flight delays.
Conducting regular surveys and gathering passenger feedback can help identify specific areas
for improvement.

2. Does Passenger Gender Affect Satisfaction?

Recommendation: Since gender does not significantly affect satisfaction, airlines should
focus on factors that influence all passengers, such as service quality, inflight entertainment,
and seating comfort. Tailored service offerings, like customized meal preferences and
entertainment choices, can help improve satisfaction across all demographics.

3. How Does Passenger Class Affect Satisfaction?

Recommendation:
• Economy-class experience should be upgraded with better seating, improved meal
quality, and additional service options.
• Airlines could introduce premium economy options with extra legroom and better
services at a slightly higher price.
• Loyalty programs should offer incentives for economy-class passengers to upgrade,
increasing their satisfaction and engagement.

4. What Is the Average Age of Satisfied vs. Dissatisfied Passengers?

Recommendation:
• For younger passengers, airlines should invest in high-speed Wi-Fi, better
entertainment options, and power outlets at every seat to keep them engaged.
• For older passengers, airlines should enhance seating comfort and meal options to
match their preferences.
• Offering personalized experiences based on passenger age groups can help increase
overall satisfaction.

5. Does Delay Time in Departure or Arrival Affect Satisfaction?

Recommendation:

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 • Airlines should focus on reducing flight delays through better scheduling, efficient
turnaround times, and improved aircraft maintenance.
• If a delay is unavoidable, proactive communication (via SMS, email, or app
notifications) can reduce passenger frustration.
• Offering compensation such as free lounge access, refreshments, or discounts for
future flights can help retain customer loyalty.

6. Is There a Relationship Between Inflight Wi-Fi and Passenger Satisfaction?

Recommendation:
• Airlines should improve inflight Wi-Fi speed and reliability, ensuring a seamless
internet experience.
• Offering free basic Wi-Fi for all passengers and charging only for premium services
could improve satisfaction.
• If Wi-Fi issues persist, airlines should clearly communicate limitations before the
flight to manage expectations.

7. Does Increasing Flight Distance Affect Passenger Satisfaction?

Recommendation:
• For long-haul flights, airlines should invest in more comfortable seating, better meal
options, and improved inflight entertainment to enhance passenger experience.
• Economy-class passengers should be offered small perks such as complimentary
snacks, pillows, and blankets to improve comfort.
• Cabin crew should be trained to provide more attentive service on longer flights to
keep passengers satisfied.

8. How Does Seat Comfort Affect Passenger Satisfaction?

Recommendation:
• Airlines should improve seat cushioning, increase legroom, and offer ergonomic
headrests to enhance passenger comfort.
• Reclining options should be optimized, especially for long-haul flights, where comfort
plays a critical role in satisfaction.
• Offering seat selection options with reasonable pricing can allow passengers to choose
seats that best suit their comfort needs.

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9. What Cleanliness Level Satisfies Passengers?
Recommendation:
• Airlines should maintain rigorous cleaning protocols and ensure seats, tray tables,
restrooms, and cabin air quality meet the highest hygiene standards.
• Providing sanitizing wipes, ensuring fresh cabin air circulation, and regular deep
cleaning between flights can help maintain a clean environment.
• Cabin crew should conduct frequent spot checks and clean-up services to maintain
hygiene throughout the flight.

10. What Is the Relationship Between Customer Type and Satisfaction?

Recommendation:
• Airlines should expand loyalty programs and offer perks such as seat upgrades,
priority boarding, and extra baggage allowances to encourage repeat customers.
• First-time travelers should be provided with clear guidance on check-in, baggage
policies, and inflight services to improve their experience.
• Airlines should personalize offers for frequent flyers by using customer data to
recommend flights, upgrades, and special deals.

11. What Food and Drink Quality Satisfies Passengers?

Recommendation:
• Airlines should improve food quality and expand menu options, especially for
passengers with dietary restrictions (e.g., vegetarian, gluten-free, vegan).
• Economy-class meal portions should be slightly increased, and better beverage
choices should be made available.
• Airlines should survey passengers about their food preferences and introduce regional
meal options to cater to diverse tastes.

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Final Recommendations for Overall Satisfaction Improvement:
Enhance economy-class experience by improving seating comfort, meal quality, and service
levels.
Minimize flight delays and provide better communication when disruptions occur.
Improve inflight Wi-Fi by offering free basic internet access and ensuring faster
connectivity.
Upgrade long-haul flight experience with better entertainment, seating, and service.
Maintain high cleanliness standards through rigorous protocols and frequent checks.
Strengthen loyalty programs to encourage repeat customers with personalized offers.
Improve food variety and portion sizes while offering healthier meal options.

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 CHAPTER 6 – LIMITATIONS AND SCOPE OF
FUTURE RESEARCH
6.1 Limitation
1. Data Limitations:
o The dataset used may not represent all airline passengers globally, as it is
limited to a specific timeframe and demographic. o The study relies on
available passenger survey data, which may include biases or incomplete
responses. o Additional data sources, such as online reviews, flight logs, and
social media mentions, could provide a more comprehensive view of
passenger sentiment.
2. Model Performance Constraints:
o The prediction model’s accuracy is dependent on feature selection and data
preprocessing techniques.
o Certain qualitative factors influencing passenger satisfaction, such as customer
service interactions and brand loyalty, are difficult to quantify. o The model
does not account for sudden changes in airline operations, such as mergers,
acquisitions, or shifts in company policies.
3. Generalization Issues:
o The results may not be applicable to all airline types, such as budget airlines
versus premium carriers.
o Variations in airline policies, regional differences, and cultural factors could
affect satisfaction levels beyond the scope of this study.
o Further validation is required using cross-regional datasets to determine how
well findings apply to different markets.
4. Technological Constraints:
o Limited access to real-time data may affect the accuracy of predictions.
o The impact of new technologies, such as AI-driven customer service and
inflight entertainment improvements, is not fully explored.
o Future studies could benefit from integrating airline industry innovations, such
as blockchain-based ticketing and biometric boarding processes.
5. External Factors:

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 o Factors like economic conditions, fuel price fluctuations, and unexpected
disruptions (e.g., pandemics, natural disasters) are not included in the analysis.
o Passenger satisfaction can be influenced by changes in airline regulations
and evolving customer expectations. o Global events, such as shifts in tourism
demand and airline safety concerns, could create additional layers of
complexity in satisfaction analysis.

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6.2 Scope for Future Research
This study provides a strong foundation for further exploration. Future research could extend
this work in the following ways:
1. Enhanced Data Collection & Real-Time Analysis:
o Incorporating real-time passenger feedback and social media sentiment
analysis to improve the model’s accuracy. o Expanding the dataset to include a
wider range of airlines, geographic regions, and passenger demographics.
o Utilizing big data analytics and cloud computing for processing large-scale
passenger feedback efficiently.
2. Improved Machine Learning Models:
o Experimenting with advanced deep learning techniques for better prediction
accuracy.
o Using ensemble models or hybrid approaches to capture complex satisfaction
patterns. o Implementing explainable AI techniques to make model predictions
more interpretable and actionable for airline management.
3. Industry-Specific Customization:
o Conducting separate studies for budget airlines, premium carriers, and regional
airlines to tailor recommendations accordingly.
o Exploring airline-specific initiatives such as loyalty programs, sustainability
efforts, and personalized in-flight experiences.
o Evaluating how different pricing strategies influence passenger satisfaction
and booking behaviors.
4. Impact of Emerging Technologies:
o Assessing the role of AI-based chatbots, automated customer service, and
smart personalization in enhancing passenger experience. o Evaluating the
impact of virtual reality (VR) and augmented reality (AR) in airport and in-
flight entertainment.
o Investigating how the integration of the Internet of Things (IoT) in aircraft can
improve in-flight comfort and service efficiency.
5. Longitudinal Studies:
o Conducting multi-year studies to observe trends in passenger satisfaction over
time. o Examining how passenger preferences evolve with technological
advancements and industry shifts.

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 o Identifying the long-term impact of airline sustainability initiatives on
passenger loyalty and perception.
6. External Factors Analysis:
o Studying the impact of fuel price volatility, global economic downturns, and
geopolitical factors on airline satisfaction trends.
o Exploring the effects of regulatory changes on passenger experience, such as
new safety protocols or environmental policies.
o Analyzing the impact of climate change policies and carbon offset programs
on passenger satisfaction and airline operations.

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 Bibliography

1. Baker, M. A., & Crompton, J. L. (2000). Quality, satisfaction, and behavioural

intentions. Annals of Tourism Research, 27(3), 785-804.
2. Chen, C. F., & Chang, Y. S. (2012). The influence of airline service quality on
passenger satisfaction. International Journal of Aviation Management, 1(2), 112-124.
3. Kwortnik, R. J., & Thompson, G. M. (2009). Unifying service marketing and
operations with service experience management. Journal of Service Research, 11(4),
389-406.
4. Li, J., & Wu, C. (2019). Predicting customer satisfaction in the airline industry: An
analysis of the impact of service quality. Journal of Travel Research, 58(1), 61-73.
5. Matzler, K., & Sauerwein, E. (2002). The Austrian Customer Satisfaction Index:
Results and implications. Total Quality Management, 13(5), 597-607.
6. Oh, H. (2001). Revisiting importance-performance analysis. Tourism Management,
22(6), 617-627.
7. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A
multipleitem scale for measuring consumer perceptions of service quality. Journal of
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8. Rhoades, D. L., & Waguespack, B. (2008). Airline service quality and passenger
satisfaction: A study of U.S. carriers. Journal of Airline and Airport Management,
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9. Silva, P. A., & Viera, F. M. (2018). The role of customer satisfaction in the airline
industry: A literature review. Journal of Air Transport Management, 74, 40-51.
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consequences of service quality. Journal of Marketing, 60(2), 31-46.

Appendix – Questionnaires

Introduction: Thank you for participating in our survey. Your feedback is valuable and will
help improve airline services. The survey will take approximately 5-10 minutes to complete.
All responses will remain confidential.
Section 1: Demographic Information
1. Age: o Under 18 o 18-24 o 25-34 o 35-44 o 45-54 o 55-64 o 65 and above

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 2. Gender: o Male o Female
o Prefer not to say
3. Travel Class:
o Economy o Business o First
Class
Section 2: Flight Experience
4. How frequently do you travel by air?
o Rarely (1-2 times a year) o
Occasionally (3-5 times a
year) o Frequently (6+
times a year)
5. What was the purpose of your travel?
o Business o Leisure o Other
(please specify): __________

6. Which airline did you fly with for this trip?

o Airline Name: __________
Section 3: Service Evaluation
7. Please rate the following aspects of your flight on a scale of 1 to 5:
o Check-in Process:
▪ 1 (Very Poor)
▪ 2 (Poor)
▪ 3 (Average)
▪ 4 (Good)
▪ 5 (Excellent) o Cabin Crew Service:
▪ 1 (Very Poor)
▪ 2 (Poor)
▪ 3 (Average)
▪ 4 (Good)
▪ 5 (Excellent) o In-flight Services (Meals,
Entertainment, etc.):
▪ 1 (Very Poor)
▪ 2 (Poor)

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 ▪ 3 (Average)
▪ 4 (Good) ▪ 5 (Excellent)
8. Was your flight on time?
o Yes o No
Section 4: Overall Satisfaction
9. How satisfied are you with your overall experience?
o 1 (Very Dissatisfied) o 2
(Dissatisfied) o 3 (Neutral) o
4 (Satisfied) o 5 (Very
Satisfied)
10. Would you recommend this airline to others?
o Yes o No Section 5: Open
Feedback

11. What did you like most about your flight?

12. What improvements would you suggest?

Thank you for your time!

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