The aviation industry always looks for better customer satisfaction, This promotes revenue and client loyalty. By utilizing machine learning, airlines can better predict the level of satisfaction among passengers and align accordingly. This is based on big data, including experience during flight, quality of service, on-time performance, and comments from customers. With such techniques, airline firms apply sophisticated algorithms and predictive models to trace passenger satisfaction patterns or trends. Findings from such analysis lead airlines into proactive improvements in services to better personalize customer experience and operational efficiency. A deeper understanding of passenger needs through machine learning in predictive satisfaction also helps to create data-driven decision making and maintain competitive superiority in the dynamic nature of the aviation sector.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Harnessing Machine Learning for Next-Level Airline Satisfaction Prediction


    Beteiligte:
    Gnanasoundharam, J. (Autor:in) / M, Sarithra S (Autor:in) / B, Thirisha (Autor:in)


    Erscheinungsdatum :

    17.04.2025


    Format / Umfang :

    324444 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Airline Fare Prediction Using Machine Learning Algorithms

    Subramanian, R. Raja / Murali, Marisetty Sai / Deepak, B et al. | IEEE | 2022



    Prediction of Airline Ticket Price Using Machine Learning Method

    Hüseyin Korkmaz | DOAJ | 2024

    Freier Zugriff

    Cost-sensitive prediction of airline delays using machine learning

    Choi, Sun / Kim, Young Jin / Briceno, Simon et al. | IEEE | 2017


    Exploratory Data Analysis and Prediction of Passenger Satisfaction with Airline services

    Salah-Ud-Din, Maham / S.S., Blessy Trencia Lincy / Al Ali, Hannah | IEEE | 2024