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    <title>An Analysis of Flight Delays in the US | DS 4200 Final Project</title>
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                <h1>An Analysis of Flight Delays in the US</h1>
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            <h1 class="gray">DS 4200 Final Project</h1>
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            <a href="about.html" class="selected-nav">About</a>
            <a href="index.html" class="nav-item">Project</a>
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            <p class="subheading">Group members</p>
            <p class="gray">Daniel Joung, Rebecca Choi, Iverson Qiu</p>
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            <p class="subheading">Topic Introduction</p>
            <p class="gray">The topic we will explore for this project is air traffic, precisely data revolving around US Domestic flights from various cities and delays. We chose this topic because air travel is a highly regulated industry with lots of variables to explore. Furthermore, we all love to travel but have had a couple of bad experiences waiting in the airport with delays. Therefore we thought it’ll be a very interesting topic where we could dive further into. As for the data set, because people are traveling all day every day every year, there are likely lots of patterns that could be drawn out of air traffic data, which we wanted to translate into simple and informative visualizations.
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            <p class="gray">For this study, we will be using 2 datasets. The first dataset contains flight performance data for many different airlines and from years between 2013 - 2023 which we extracted from Kaggle. Some of these flight carriers include ones that we have flown with in the past including Jet Blue, Delta, American, and United. The dataset includes arrival statistics across multiple airports, detailing the number of flights, delays, cancellations, and diversions. The dataset tracks various causes of delays, such as carrier-related, weather, National Airspace System, security, and late aircraft delays. It consists of attributes such as the airport name, flight counts, delay durations, and disruption counts. This data is valuable for analyzing airline punctuality, identifying delay patterns, and assessing operational efficiency.<br /><br />The other dataset we will be using is from the Bureau of Transportation Statistics Website which contains performance data of US airlines based on income. While the dataset contains subsectors of income such as quarters, international, domestic, and Latin America income, we just used the overall income for a simple income to delay correlation analysis. By merging the main airline dataframe with this simple income/loss dataframe, we will be able to visualize relationships between the 2 datasets.
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                    <li class="gray"><a class="gray underline link-p" href="https://www.collinsaerospace.com/what-we-do/capabilities/connected-ecosystem/feature-stories/how-data-unlocks-huge-benefits-in-the-aviation-industry">How data unlocks huge benefits in the aviation industry | Collins Aerospace</a></li>
                    <li class="gray"><a class="gray underline link-p" href="https://link.springer.com/article/10.1007/s43621-024-00781-7?utm_source=chatgpt.com">A review on air traffic flow management optimization: trends, challenges, and future directions | Discover Sustainability</a></li>
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