Predicting Flight Delays with Machine Learning: A Case Study from Saudi Arabian Airlines

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/ijae/volume-2024/3385463/figures/3385463.fig.002a.svgz" name="3385463.fig.002a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>The user-friendly web application</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/ijae/volume-2024/3385463/figures/3385463.fig.002b.svgz" name="3385463.fig.002b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>The dashboard</td></tr></table>

<div>(a) The user-friendly web application that we have implemented to predict domestic flight delays in Saudi Arabia. (b) We have also provided a dashboard with a detailed analysis of the data in the Saudi flight dataset, presenting various visualizations and insights.</div>

International Journal of Aerospace Engineering

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Figure 2: Predicting Flight Delays with Machine Learning: A Case Study from Saudi Arabian Airlines 