International flight fare prediction and analysis of factors impacting flight fare

Abstract

In the rapidly evolving landscape of global travel, understanding international flight prices has become pivotal for both travellers and airlines. This paper delves into the intricate web of factors influencing flight prices, utilizing a dataset from Ease My Trip spanning 50 days. Employing rigorous data processing techniques, including handling missing values and label encoding, the study explores correlations between various parameters such as cabin class, flight numbers, airlines, and duration, shedding light on pricing dynamics. The research employs linear regression, decision trees, and random forest models for prediction. The results showcase the significance of class, flight numbers, and duration on prices. Particularly, higher cabin classes correlate strongly with increased prices, offering vital insights for airlines to optimize revenue. The models predictive accuracies are commendable, with the random forest model standing out, explaining 98.9% of the variance. This study not only illuminates the complex interplay of factors steering international flight prices but also provides airlines with robust pricing strategies. The findings empower travellers to make informed decisions, promising a harmonious future for the aviation industry in an ever-changing global market.