Which algorithms are used in this passenger satisfaction prediction project?

The system implements and compares two classification algorithms: Decision Tree and Logistic Regression. The Decision Tree classifier achieved the best performance with 89.24% accuracy and an F1-score of 89.23%.

What features are used for passenger satisfaction prediction?

The system uses features including passenger demographics (age, gender), travel context (type of travel, class, customer type), operational factors (flight distance, delays), and 14 service quality ratings (Online Boarding, Seat Comfort, Inflight Entertainment, Food, Cleanliness, etc.)

What is the accuracy of this passenger satisfaction model?

The Decision Tree model achieves 89.24% test accuracy with a precision of 89.31%, recall of 89.24%, and ROC-AUC of 0.9456, making it highly reliable for passenger satisfaction prediction.

Who can benefit from this airline satisfaction prediction system?

Airlines can use it to proactively identify dissatisfied passengers, optimize service delivery, and implement targeted improvements. Customer service teams can prioritize at-risk passengers. Operations teams can identify service quality issues before they impact satisfaction.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Watch Demo Video

Machine Learning

Airline Passenger Satisfaction Prediction Using Machine Learning

The Airline Passenger Satisfaction Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts whether an airline passenger will be satisfied or dissatisfied with their travel experience. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It uses advanced algorithms like Decision Tree and Logistic Regression to achieve 89.24% accuracy.

The system leverages 25+ features including passenger demographics, flight details, and 14 service quality ratings (Online Boarding, Seat Comfort, Inflight Entertainment, Food, Cleanliness, etc.). It provides interactive visualizations, feature importance analysis, model comparison, and real-time satisfaction prediction to help airlines proactively manage passenger experience and improve service quality.

Python 3.8+ Machine Learning Decision Tree Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Binary Satisfaction Classification
Decision Tree (89.24% Accuracy)
Logistic Regression (88.70% Accuracy)
14 Service Quality Ratings
Feature Importance Analysis
Real-time Satisfaction Prediction
Model Performance Comparison
Interactive Visualizations
ROC-AUC & Confusion Matrix
Flask Web Application

Algorithms Used

🌳 Decision Tree Classifier

Non-parametric tree-based model, captures complex feature interactions, highly interpretable

🎯 Accuracy: 89.24%

📊 Logistic Regression

Linear probabilistic model with L2 regularization, provides interpretable coefficients

🎯 Accuracy: 88.70%

🔍 5-Fold Stratified CV

Cross-validation for model stability and generalization assessment

📊 DT: 90.00% ± 1.50%

📈 ROC-AUC

Model discrimination ability assessment

📊 DT: 0.9456

Methodology & Workflow

1 Data Loading & Inspection
Airline dataset with 130,000+ records and 25 features

2 Data Preprocessing
Cleaning, encoding categorical variables, feature scaling

3 Exploratory Data Analysis
Comprehensive visualizations for data understanding

4 Model Training
Decision Tree & Logistic Regression with optimized parameters

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC, 5-fold CV

6 Web Deployment
Flask web app for real-time satisfaction prediction

Model Performance Comparison

Metric	Decision Tree	Logistic Regression	Best
Accuracy	0.8924	0.8870	Decision Tree
Precision	0.8931	0.8872	Decision Tree
Recall	0.8924	0.8870	Decision Tree
F1-Score	0.8923	0.8868	Decision Tree
ROC-AUC	0.9456	0.9362	Decision Tree
CV Mean (5-Fold)	0.9000	0.8950	Decision Tree

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Dataset (130,000+ records) Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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