Comparative Analysis of Machine Learning Models for Accurate Flight Price Prediction

Abstract

Flight fare prediction is a vital component in helping consumers make informed decisions regarding travel expenses. Airline ticket prices fluctuate due to a variety of factors such as demand, time of purchase, and flight routes. In this research, we propose a machine learning-based solution for predicting flight fares using historical data. Models like Random Forest, Gradient Boosting, and Support Vector Machines (SVM) are employed to analyze flight data and produce reliable predictions. This study demonstrates how predictive models can benefit customers by offering insights into pricing trends, thus optimizing their flight booking process.