  This research investigates flight delay trends by examining factors such as
departure time, airline, and airport. It employs regression machine learning
methods to predict the contributions of various sources to delays. Time-series
models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline
regression models such as Multiple Regression, Decision Tree Regression, Random
Forest Regression, and Neural Network. Despite considerable errors in the
baseline models, the study aims to identify influential features in delay
prediction, potentially informing flight planning strategies. Unlike previous
work, this research focuses on regression tasks and explores the use of
time-series models for predicting flight delays. It offers insights into
aviation operations by independently analyzing each delay component (e.g.,
security, weather).
