Date of Publication :25th July 2024

Abstract:The rapid and substantial fluctuations in airline ticket prices present a significant challenge in today’s dynamic market. Prices can exhibit considerable variability, even within a short span, for identical flights. Airlines strategically adjust fares based on factors such as seasonal trends and time duration, particularly for business-related travel. Profit optimization strategies involve employing diverse calculation methods, including segregating demand based on expectations and perceived value. Each airline employs unique criteria and algorithms to determine pricing, leveraging tools such as machine learning, artificial intelligence, and deep learning. This research paper focuses on utilizing machine learning algorithms, such as Random Forest, Linear Regression, KNN and Gradient Boost, using Randomized Search and Grid Search, to analyse and predict air travel expenses. By considering fundamental information such as Airline, Source, Destination, Duration, Total Stops, and other relevant factors, this study aims to forecast flight expenses accurately within a specific timeframe.

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