Airline Passenger Satisfaction Analysis

This project aims to analyze and understand airline passenger satisfaction based on a given dataset. The dataset includes various features related to airline services and customer feedback, which we'll explore to gain insights into factors influencing passenger satisfaction.

Table of Contents

• Introduction
• Dataset
• Methodology
• Exploratory Data Analysis
• Feature Importance
• Modeling
• Results
• Conclusion
• Usage
• Contributing
• License

Introduction

Airline passenger satisfaction is a critical factor for the aviation industry. Understanding what aspects of the passenger experience contribute to higher satisfaction levels can lead to improved services and increased customer loyalty. In this project, we analyze a dataset to uncover patterns and insights related to passenger satisfaction.

Dataset

The dataset used for this analysis is sourced from source_dataset_link. It contains a collection of features such as flight distance, inflight services, cleanliness, departure/arrival time satisfaction, and more. The dataset was preprocessed to handle missing values and encode categorical variables.

Methodology

1. Data Preprocessing: Cleaning the dataset, handling missing values, and encoding categorical variables.

2. Exploratory Data Analysis (EDA): Analyzing and visualizing the data to identify trends, correlations, and patterns related to passenger satisfaction.

3. Feature Importance: Determining which features have the most significant impact on passenger satisfaction.

4. Modeling: Building a predictive model to classify passenger satisfaction based on the available features.

Exploratory Data Analysis

During the EDA phase, we performed various analyses, including:

• Distribution of satisfaction levels.
• Correlations between different features and passenger satisfaction.
• Visualizations of key metrics such as flight distance, service ratings, etc.

Feature Importance

Based on our analysis, we determined the most influential factors contributing to passenger satisfaction. This information can guide airlines in focusing on the right areas to improve passenger experience.

Modeling

We utilized machine learning techniques to build a predictive model for passenger satisfaction. The dataset was split into training and testing sets, and different algorithms were experimented with, including Random Forest, Logistic Regression, and Gradient Boosting.

Results

Our model achieved an accuracy of XX% on the test set, indicating its ability to predict passenger satisfaction based on the provided features. The most important features influencing satisfaction were found to be [list_top_features_here].

Conclusion

This project demonstrates the importance of analyzing passenger satisfaction in the airline industry. By understanding the factors that contribute to higher satisfaction levels, airlines can make targeted improvements to their services, potentially leading to increased customer loyalty and positive reviews.

Usage

To replicate or build upon this project:

1. Clone this repository.
2. Download the dataset from source_dataset_link and place it in the data directory.
3. Use Jupyter Notebook or your preferred environment to run the analysis scripts.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request to this repository.

License

This project is licensed under the MIT License.