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    Flight Delay Prediction
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kurt, Mustafa; Taş Küten, Duygu
    This study aims to create a model to predict flight departure delays. Various factors might affect a flight delay, and thus different features might be selected as input to create a model concerning priorities and the power of control over the features for the party who makes the analysis. In this study, domestic commercial flights in the U.S. operated in August 2018 are studied. Besides, airplane, passenger boarding, and cargo data are combined with flight data to benefit from possible insights related to these factors. For predicting the flight delays, machine learning methods such as decision trees, random forest, bagging classifier, extra trees classifier, gradient boosting and xgboost classifier are used and results are analyzed. Further studies could be adding extra features such as data related to flight planning, personnel data, loading data, data about technical processes to prepare a plane to a flight to improve prediction capacity.