Flight Ticket Fare Prediction using Supervised Learning

Abstract

Abstract: With the increasing interconnectivity of air routes worldwide, air travel has become a widespread and expeditious means of transportation. Forecasting airline fares poses a significant and intricate challenge due to their continual fluctation, influenced by a diverse array of factores. Extensive research suggests that the application of Machine Learning, and Deep Learing techniques enables the swift estimation of flight fares at specific intervals. This study employs a Machine Learning Regression methodology to predict flight fares based on essential parameters such as departure and arrival times, departure location, destination, stopovers, and airline provider. This research employs two distinct datasets for training and testing purposes, evaluating a range of machine learning approaches to forecast flight ticket prices.