Flight Fare Prediction System
Flight Fare Prediction System
Authors:
Rishabh Goswami1, Ms. Kinjal Kevin Gandi2
1Student, Department of Computer Science & Engineering, Parul University, Vadodara, Gujarat, India
2Assistant. Professor, Department of Computer Science & Engineering, Parul University, Vadodara, Gujarat, India
ABSTRACT:
Flight ticket prices fluctuate dynamically based on multiple factors such as travel date, number of stops, and flight duration. Predicting these prices helps travelers plan cost-efficient journeys and aids airline companies in pricing strategies. This research presents a machine-learning-based flight fare prediction system built using a Random Forest Regressor. The model was trained on a cleaned and pre-processed dataset and deployed using a Flask-based web application. The system takes user inputs such as total stops, journey date, month, departure hour, and duration hours to estimate the fare. The results show that Random Forest provides reliable predictions, making this system a useful tool for real-time fare estimation.