Flight Price Prediction Using Machine Learning & AI (Time-Series Analytics Variant)

Flight Price Prediction Using Machine Learning & AI (Time-Series Analytics Variant)

For both travelers and airlines, timing is money: booking too early or too late can cost hundreds in fare differences. This analytics-focused variant of the Flight Price Prediction Using Machine Learning and AI project emphasizes time-series forecasting and comparative model benchmarking for maximum predictive power. Using a large real-world flight booking dataset scraped from the Ease My Trip platform, the project begins with rigorous statistical analysis — hypothesis testing to validate which features (airline, route, class, stops, departure time, days-left) significantly influence pricing, followed by correlation and distribution studies. The modeling phase trains and benchmarks a full spectrum of algorithms: Linear Regression (baseline), Decision Tree, Random Forest, XGBoost, LightGBM, and time-series models (Prophet/ARIMA components) for route-level fare trend forecasting. Advanced feature engineering includes cyclical encoding of departure times, route decomposition, and booking-window segmentation. The champion model is deployed through an interactive Streamlit/Flask web application where users input trip details and receive predicted fares with confidence ranges, plus a best-time-to-book recommendation engine. Comprehensive evaluation uses R2, MAE, RMSE, and residual analysis, all visualized in professional dashboards. This is a complete end-to-end machine learning project — ideal for data science portfolios.

Components

Python 3.8+
Pandas & NumPy
XGBoost & LightGBM
Prophet / ARIMA
Scikit-learn
Streamlit / Flask

Key Features

• Statistical hypothesis testing on airline, route, class, stops and timing features
• Prophet/ARIMA time-series models for route-level fare trend forecasting
• Full regression benchmarking from Linear Regression to LightGBM
• Cyclical encoding and booking-window segmentation feature engineering
• Fare estimates with confidence ranges and a best-time-to-book engine

Applications

Data science portfolios, travel analytics teams and students seeking deployment-ready end-to-end ML projects.