Next-Gen Airfare Predictions: A Data Driven Approach

Airfare changes from demand, airline policies, season, and fuel prices. Airfare prediction is a tricky balancing act that can help both consumers and airline companies. The aim of this study is to propose a ma chine learning method for predicting the air fares using the historical flying data, data preprocessing mechanisms, and popular regression models. We propose a model that achieves better predictions using Random Forest, Gradient Boosting, and Support Vector Machines [1] [2].An in-depth system architecture is proposed, and experimental results along with comparative performance graphs are presented. The high RNNMAE across all road users and the high GPS-MAE for the bus and the taxi indicate that the cutting edge of RNN-based algorithms is significantly improving the accuracy of price estimation and, thus, is reducing the uncertainty for travelers [3] [4].This study is not just limited to examining predictive techniques but also to the practical challenges faced in implementation of airfare prediction in real-time on a large scale [5] [6]. The results emphasize the need to include external influences like demand forecast, fuel price changes and economic indicators for improving the prediction accuracy [5] [7].Moreover, the proposed system can assist travelers with cost-effective choices, airlines with pricing optimization, and travel agencies with customer satisfaction improvement.