Flight fares are known to be dynamic and can change rapidly due to several factors such as seasonality, demand, competition, and fuel prices. Predicting future ticket prices accurately is an important task for airlines, travel agencies, and customers. In recent years, machine learning techniques (MLT) have been increasingly used to forecast flight fares based on historical pricing data, weather conditions, and other relevant factors. This paper proposes a machine learning approach for flight fare forecasting that utilizes various features such as departure time, arrival time, airline, route, and historical prices. The proposed model is trained using a dataset of historical flight prices and other relevant data. The performance of the model is evaluated based on several metrics such as mean absolute error and mean squared error. The results of the experiments show that the proposed machine learning approach can accurately forecast flight fares with low error rates. This can help airlines and travel agencies to optimize their pricing strategies and provide customers with more accurate price information. In addition, customers can use this approach to make more informed decisions when purchasing airline tickets. Overall, this paper presents a novel approach to flight fare forecasting that has the potential to revolutionize the airline industry and improve the customer experience.
Flight Fare Forecasting: A Machine Learning Approach to Predict Ticket Prices
Lect. Notes in Networks, Syst.
International Conference on Data Analytics and Insights ; 2023 ; Kolkata, India May 11, 2023 - May 13, 2023
Proceedings of International Conference on Data Analytics and Insights, ICDAI 2023 ; Kapitel : 60 ; 703-713
2023-07-25
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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