airline-satisfaction-model

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✈️ Airline Customer Satisfaction Predictor

A machine learning application for predicting airline passenger satisfaction based on travel experiences and service ratings.

Project Overview

This project analyzes airline customer feedback data to identify factors influencing passenger satisfaction. Using various Random Forest, the system predicts whether a customer will be satisfied with their travel experience based on service quality metrics, flight details, and passenger demographics.

Repository Structure

AIRLINE/
├── data/                            # Data files
│   ├── Invistico_Airline.csv        # Original dataset
│   └── processed_data.csv           # Preprocessed data
├── models/                          # Saved ML artifacts
│   ├── best_ml_model.pkl            # Optimized ML model
│   ├── label_encoders.pkl           # Categorical encoders
│   └── scaler.pkl                   # Feature scaler
├── results/                         # Analysis outputs
│   └── model_performance_comparison.png
└── scripts/                         # Core code modules
    ├── data_cleaning.py             # Data preprocessing
    ├── exploratory_data_analysis.py # EDA visualizations  
    ├── model_training.py            # Model selection & training
    ├── run_all.py                   # Pipeline orchestration

Setup and Installation

# Clone repository
git clone 
cd airline

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

# Run the complete pipeline
python scripts/run_all.py

# Launch the Streamlit prediction interface
streamlit run app.py

Methodology

The project follows a standard data science workflow:

1. Data Preprocessing: Handling missing values, encoding categorical variables
2. Exploratory Analysis: Visualizing patterns and relationships in passenger feedback
3. Model Development: Training and evaluating multiple classifiers (Random Forest, Logistic Regression, etc.)
4. Deployment: Interactive web application for real-time predictions

Key Results

The model successfully identifies the most significant factors affecting passenger satisfaction, achieving 96% prediction accuracy.

Author

Sujnan J Kalmady

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license: mit

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