✈️ Airline Passenger Satisfaction Data Analysis

A collaborative data analysis project by @kaganmart9 & @ahakanortacbayram

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📌 Project Overview

This project explores airline passenger satisfaction using data-driven methods. The objective is to understand how demographic and service-related factors impact customer satisfaction in commercial air travel.

Using structured and clean code practices in Python, we preprocess, analyze, and visualize data from a real-world airline passenger dataset.

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🧠 Key Questions Addressed

• How does age affect passenger satisfaction?
• Is there a difference in satisfaction between genders?
• Does flight distance impact satisfaction levels?
• Which services (e.g., cleanliness, baggage, online booking) are most correlated with satisfaction?

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## 🗂️ Project Structure

AIRLINE-PASSENGER-SATISFACTION-DATA-ANALYSIS/
├── data/
│   ├── raw/                           # Raw dataset (e.g., raw-data.csv)
│   └── processed/                     # Cleaned dataset (e.g., processed.csv)
│
├── docs/
│   └── project-plan.pdf              # Project planning and documentation
│
├── notebooks/                        # Jupyter notebooks for EDA & prototyping
│   ├── 03-analysis-b-age-satisfaction.ipynb
│   ├── 04-analysis-b-gender-satisfaction.ipynb
│   └── flight-distance-satisfaction-scatter-analysis.ipynb
│
├── reports/
│   ├── figures/                      # All generated visualizations (PNG)
│   │   ├── age_group_satisfaction_bar.png
│   │   ├── gender_satisfaction_pies_clean.png
│   │   └── ...
│   └── tables/                       # Output statistics (CSV)
│       ├── flight_distance_satisfaction_stats.csv
│       └── gender_satisfaction_percent.csv
│
├── src/                              # Modular & reproducible Python scripts
│   ├── config.py                     # Path config
│   ├── cleaning.py                   # Data cleaning pipeline
│   ├── age_satisfaction_prc.py       # Age-based satisfaction analysis
│   ├── gender_satisfaction.py        # Gender-based satisfaction analysis
│   ├── distance_satisfaction.py      # Flight distance analysis
│   └── service_correlation.py        # Service factor correlations
│
├── requirements.txt                  # Python dependencies
└── README.md                         # Project documentation (this file)

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🛠️ Setup & Installation

🐍 Python version: 3.11.x

1. Clone the repository:

git clone https://github.com/kaganmart9/airline-passenger-satisfaction-data-analysis.git
cd airline-passenger-satisfaction-data-analysis

1. (Optional) Create a virtual environment:

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

1. Install dependencies:

pip install -r requirements.txt

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🚀 How to Run Analyses

Each script is modular and reproducible. You can run them independently.

➤ Clean the raw dataset

python src/cleaning.py

➤ Analyze satisfaction by age

python src/age_satisfaction_prc.py

➤ Analyze satisfaction by gender (CLI options available)

python src/gender_satisfaction.py --show --timestamp

➤ Analyze satisfaction vs. flight distance

python src/distance_satisfaction.py

➤ Analyze service feature correlations

python src/service_correlation.py

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📊 Sample Visualizations

🎯 Satisfaction by Age Group

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🧓 Age-wise Pie Charts

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👨‍🦰 Gender Satisfaction Pie Charts

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📏 Flight Distance Impact

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📈 Output Tables

• flight_distance_satisfaction_stats.csv
• gender_satisfaction_percent.csv

All are saved under: reports/tables/

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👥 Contributors

Name	GitHub Handle
Ali Kağan Mart	@kaganmart9
Ahmet Hakan Ortaçbayram	@ahakanortacbayram

• Both contributors collaborated equally on all parts of the project, including:

• Data preprocessing & cleaning

• Exploratory data analysis (EDA)

• Modular Python scripting

• Visualization and reporting

• Workflow design and documentation

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

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🙌 Acknowledgements

Special thanks to the open data contributors that provided the airline passenger satisfaction dataset.