A Holistic Approach on airfare price prediction

Abstract

Predicting airfare prices accurately is essential for both travelers and airline industry stakeholders.Traditional methods often fail to capture the complexity of airfare dynamics, leading to inaccurate predictions.In this research, we propose a holistic approach to airfare price prediction utilizing machine learning techniques.Our methodology integrates diverse data sources and advanced ML algorithms to consider various factors such as seasonality, route popularity, demand fluctuations, fuel costs, economic indicators, and social media sentiments.By leveraging these factors, our model aims to provide more accurate and timely predictions of airfare prices.We conduct experiments to evaluate the effectiveness of our approach, comparing it with baseline models.The results demonstrate the superior performance of our holistic approach, highlighting its potential to enhance decision-making for travelers and industry stakeholders alike.