Flight Price Prediction Using Machine Learning & AI

Flight Price Prediction Using Machine Learning & AI

Airfare prices fluctuate dynamically based on demand, seasonality, route competition, and booking timing — leaving travelers uncertain about when to book. This project builds an intelligent flight fare forecasting system that helps travelers and travel businesses predict ticket prices with data-driven accuracy. The system is trained on a real-world flight booking dataset sourced from the Ease My Trip platform, containing over 300,000 records with features such as airline, source and destination cities, departure/arrival times, number of stops, class, and days-until-departure. The pipeline includes comprehensive exploratory data analysis (EDA), statistical hypothesis testing, and feature engineering (cyclical time encoding, route tokenization, one-hot and target encoding) before training regression models including Linear Regression, Random Forest, XGBoost, and LightGBM. Model performance is evaluated using R2 score, MAE, and RMSE, with the best model deployed behind a Flask/Streamlit web application that accepts trip parameters and returns instant price estimates along with a "book now vs. wait" recommendation. Interactive analytics dashboards visualize price trends by airline, route, and booking window. This is a top-rated machine learning regression project for data science students and travel-tech developers.

Components

Python 3.8+
Pandas & NumPy
Scikit-learn
XGBoost & LightGBM
Flask / Streamlit
Plotly dashboards

Key Features

• Training on a 300,000+ record EaseMyTrip flight booking dataset
• Exploratory data analysis and statistical hypothesis testing
• Cyclical time encoding, route tokenization and target encoding feature engineering
• Linear Regression, Random Forest, XGBoost and LightGBM benchmarking with R2, MAE, RMSE
• Book-now-vs-wait recommendation engine with price trend dashboards

Applications

Travel-tech platforms, fare comparison services, data science students and anyone building an end-to-end ML portfolio project.