Paper Contents
Abstract
The airline industry operates in a highly dynamic pricing environment where ticket prices fluctuate based on multiple factors, including demand, seasonality, competition, fuel costs, and consumer behavior. Traditional pricing models struggle to capture these complex interactions, leading to suboptimal forecasting and decision-making. With the advent of machine learning, AI-driven models can analyze vast datasets, identify hidden patterns, and improve the accuracy of flight price predictions. This research explores the application of machine learning algorithms, such as regression models, decision trees, ensemble methods (XGBoost, Random Forest), and deep learning techniques (LSTM), to understand market dynamics and enhance pricing strategies. By integrating economic indicators, weather data, and social media sentiment, this study aims to provide actionable insights for airlines, travel aggregators, and consumers, optimizing pricing decisions and improving transparency in airfare forecasting.v
Copyright
Copyright © 2025 Ganesh Shrimant Giri. This is an open access article distributed under the Creative Commons Attribution License.
