Airline Passenger Classification using Machine Learning

📌 Overview

This project focuses on classifying airline passengers based on their travel experience and service-related attributes. Machine learning classification models are used to predict passenger categories.

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🎯 Problem Statement

Understanding passenger behavior is crucial for improving airline services. The objective of this project is to classify passengers using service quality, travel type, and flight-related features.

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📊 Dataset Description

The dataset contains structured passenger and service-related data.

Features

• Gender
• Customer Type
• Type of Travel
• Class
• Flight Distance
• Inflight wifi service
• Departure/Arrival time convenient
• Ease of Online booking
• Seat comfort
• Inflight entertainment
• On-board service
• Cleanliness

Target Variable

• Satisfaction
  • Satisfied
  • Neutral or Dissatisfied

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🛠️ Methodology

1. Data inspection and preprocessing
2. Handling categorical and numerical features
3. Feature encoding
4. Train-test split
5. Classification model training
6. Model evaluation

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🤖 Models Used

• Logistic Regression
• Decision Tree
• Random Forest

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📈 Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-score

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🔍 Key Insights

• Service quality features strongly influence passenger satisfaction
• Inflight entertainment and seat comfort are key indicators
• Ensemble models improve classification performance

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🚀 Future Improvements

• Hyperparameter tuning
• Feature importance visualization
• Deploy as a web application
• Apply explainable AI techniques

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🧠 Learnings

• Classification on structured real-world data
• Handling categorical features
• Interpreting customer satisfaction models