Authors: Reddi Sri Venkata Lakshmi Lahari, K Ravi Kumar
Abstract: The increasing volatility of airline ticket pricing, driven by dynamic market demand, seasonal variations, operational costs, and competitive pricing strategies, has made accurate flight fare prediction a challenging task. Conventional forecasting approaches often struggle to model the nonlinear and time-dependent relationships that influence airfare fluctuations. This paper presents an intelligent web-based flight price prediction framework that integrates advanced machine learning and deep learning techniques to deliver accurate and real-time airfare forecasts. The proposed system employs comprehensive data preprocessing, feature engineering, and sequential learning to capture complex pricing patterns from historical flight data. A Long Short-Term Memory (LSTM) network is utilized to model temporal dependencies, while adaptive learning mechanisms enable the framework to remain responsive to continuously changing market conditions. The developed web application provides users with an interactive platform for obtaining instant fare predictions, thereby supporting informed travel planning and strategic pricing decisions. Experimental evaluation demonstrates that the proposed framework achieves reliable predictive performance and effectively models dynamic airfare trends. The proposed solution offers a scalable, data-driven decision support system for airlines, travel agencies, and passengers, contributing to the advancement of intelligent transportation analytics and real-time predictive systems.
DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.151
