SkyFare AI: A Generative AI-Powered Web Platform for Real-Time Flight Price Prediction

Authors- P. V Komali, Pentapati Prasanthi, Saride Swetha Rama Veni, Ayinala Bhargavi Sowmya, Asritha Goppu, Bhula Vivek

Abstract-The dynamic nature of the aviation industry presents significant challenges in accurately predicting flight fares, as factors like fluctuating demand, fuel price shifts, and route complexities contribute to pricing unpredictability. This research introduces a novel approach that harnesses generative artificial intelligence (GAI) to improve real-time airfare forecasting. By incorporating generative models, deep learning techniques, and historical fare trends, the system enhances prediction accuracy. A GAI-driven web-based framework enables the model to analyse intricate patterns and interdependencies within historical airline data, allowing it to detect complex relationships and adjust to evolving market conditions. Using deep neural networks, the model processes multiple influencing factors, extracting crucial insights for a deeper understanding of airfare variations. Furthermore, the proposed approach emphasizes real-time forecasting precision, facilitating swift responses to market fluctuations and serving as an effective tool for optimizing airfare pricing strategies.

DOI: /10.61463/ijset.vol.13.issue2.223