Exploring Predictive Models for Airfare Forecasting: A Comparative Analysis of Time Series and Machine Learning Approaches

Accurate prediction of airfare prices is essential for both travelers and airlines alike.This paper presents a comprehensive investigation into airfare prediction, focusing on the context of Indian Airways.Leveraging a dataset specific to Indian Airways flight records, spanning both historical and future data, this research explores the efficacy of various predictive modeling techniques.Machine learning algorithms and time series analysis are employed for comparative analysis, evaluating their performance in predicting airfare prices.Through rigorous experimentation and evaluation, this study provides valuable insights into the strengths and limitations of different methodologies.The findings contribute to advancing the understanding of airfare forecasting techniques tailored to the unique characteristics of Indian Airways data.This research not only facilitates informed decision-making for travelers but also aids airlines in strategic pricing