Flight Fare Prediction Using Machine Learning

Abstract

ABSTRACT Flight fare prediction using machine learning has emerged as a crucial application in the travel industry, enabling airlines, travel agencies, and consumers to make more informed decisions. This study aims to develop a machine learning model to predict flight fares based on historical data, flight characteristics, and various factors influencing pricing. By leveraging algorithms such as Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, the model analyzes features including departure time, flight duration, airline, origin and destination cities, seasonality, and demand fluctuations. The goal is to provide accurate predictions of future flight prices, enabling travelers to find optimal booking times and assisting airlines in dynamic pricing strategies. The model's performance is evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), with the results demonstrating the potential of machine learning in offering significant improvements over traditional methods of fare prediction.