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+<h2 id="introduction">Introduction</h2>
+<p><em>The dynamics of flight price predictions</em></p>
+<p>Flight pricing models have long been of interest to researchers,
+airline companies, and consumers alike <span class="citation"
+data-cites="sun2024airline">(Sun et al. 2024)</span>. As global air
+travel expands, so do the complexities involved in predicting flight
+prices due to the dynamic nature of factors such as fuel costs, demand
+fluctuations, and the regulatory environment <span class="citation"
+data-cites="sun2024airline belobaba2015global international2019economic borenstein1994competition">(Sun
+et al. 2024; Belobaba, Odoni, and Barnhart 2015; Association et al.
+2019; Borenstein and Rose 1994)</span>. Predicting flight prices
+accurately allows both passengers and airlines to optimize travel
+schedules, with potential economic and environmental benefits <span
+class="citation" data-cites="belobaba2015global">(Belobaba, Odoni, and
+Barnhart 2015)</span>. In recent years, machine learning techniques have
+been widely adopted in various industries, including aviation, to model
+complex relationships and predict outcomes such as pricing <span
+class="citation"
+data-cites="sherly2023machine kalampokas2023holistic xu2021influential">(Sherly
+Puspha Annabel et al. 2023; Kalampokas et al. 2023; Xu et al.
+2021)</span>. For this project, we propose a machine learning-based
+predictive model designed to estimate future flight prices based on
+historical data from domestic flight routes to and from India.</p>
+<p><em>The importance of predicting flight prices</em></p>
+<p>Accurately predicting flight prices has significant implications for
+multiple stakeholders. For consumers, anticipating price trends helps
+optimize travel costs, allowing for better budgeting and planning <span
+class="citation"
+data-cites="international2019economic borenstein1994competition gillen2005economics">(Association
+et al. 2019; Borenstein and Rose 1994; Gillen and Morrison 2005)</span>.
+Airlines, on the other hand, can improve their revenue management
+strategies through dynamic pricing, ensuring that flights operate closer
+to capacity while adjusting pricing to meet seasonal demand shifts and
+market competition <span class="citation"
+data-cites="belobaba2015global borenstein1994competition gillen2005economics">(Belobaba,
+Odoni, and Barnhart 2015; Borenstein and Rose 1994; Gillen and Morrison
+2005)</span>. Additionally, by integrating environmental factors such as
+carbon emissions into the pricing model, this research can help airlines
+design more sustainable routes, potentially leading to fewer emissions
+through better traffic management and efficient flight operations <span
+class="citation"
+data-cites="sun2024airline international2019economic gillen2005economics xiong2023aviation">(Sun
+et al. 2024; Association et al. 2019; Gillen and Morrison 2005; Xiong et
+al. 2023)</span>. The aviation industry contributes significantly to the
+global economy but also poses challenges due to its substantial carbon
+footprint <span class="citation"
+data-cites="brueckner2017airline">(Brueckner and Abreu 2017)</span>.</p>
+<p><em>Methodological approach and dataset</em></p>
+<p>This project utilizes a Kaggle dataset on domestic flights in India,
+covering variables such as airline name, total stops, departure and
+destination locations, and flight prices [3–5, 8]. Using regression
+techniques within a machine learning framework, the researchers aim to
+identify which variables most significantly impact flight prices.
+Initial findings suggest that factors such as the number of stops,
+flight duration, and seasonal demand (correlating with major holidays in
+India) are key drivers of price variation. For instance, a notable spike
+in flight prices is observed around holidays in India, are insights that
+not only advance knowledge in transportation economics but also serve as
+a crucial decision-making tool for airline pricing strategies <span
+class="citation"
+data-cites="sun2024airline international2019economic borenstein1994competition gillen2005economics">(Sun
+et al. 2024; Association et al. 2019; Borenstein and Rose 1994; Gillen
+and Morrison 2005)</span>.</p>
+<p><em>Multidisciplinary implications</em></p>
+<p>By developing this predictive model, the research bridges the gap
+between economics, machine learning, and environmental sustainability.
+Future applications of this work could extend beyond pricing into areas
+like route optimization and emission reduction strategies <span
+class="citation" data-cites="international2019economic">(Association et
+al. 2019)</span>. As flight prices are a key variable in both economic
+and environmental calculations, understanding their underlying dynamics
+can contribute to more sustainable and cost-effective air travel in the
+future <span class="citation"
+data-cites="belobaba2015global international2019economic gillen2005economics">(Belobaba,
+Odoni, and Barnhart 2015; Association et al. 2019; Gillen and Morrison
+2005)</span>.</p>
+<div id="refs" class="references csl-bib-body hanging-indent"
+data-entry-spacing="0" role="list">
+<div id="ref-international2019economic" class="csl-entry"
+role="listitem">
+Association, International Air Transport et al. 2019. <span>“Economic
+Performance of the Airline Industry.”</span> <em>URL: Https://Www. Iata.
+Org/Whatwedo/Documents/Economics/IATA-Economic-Performance-of-the-Industry-End-Year-2014-Report.
+Pdf (Дата Обращения: 22.12. 2015)</em>.
+</div>
+<div id="ref-belobaba2015global" class="csl-entry" role="listitem">
+Belobaba, Peter, Amedeo Odoni, and Cynthia Barnhart. 2015. <em>The
+Global Airline Industry</em>. John Wiley &amp; Sons.
+</div>
+<div id="ref-borenstein1994competition" class="csl-entry"
+role="listitem">
+Borenstein, Severin, and Nancy L Rose. 1994. <span>“Competition and
+Price Dispersion in the US Airline Industry.”</span> <em>Journal of
+Political Economy</em> 102 (4): 653–83.
+</div>
+<div id="ref-brueckner2017airline" class="csl-entry" role="listitem">
+Brueckner, Jan K, and Chrystyane Abreu. 2017. <span>“Airline Fuel Usage
+and Carbon Emissions: Determining Factors.”</span> <em>Journal of Air
+Transport Management</em> 62: 10–17.
+</div>
+<div id="ref-gillen2005economics" class="csl-entry" role="listitem">
+Gillen, David, and William G Morrison. 2005. <span>“The Economics of
+Franchise Contracts and Airport Policy.”</span> <em>Journal of Air
+Transport Management</em> 11 (1): 43–48.
+</div>
+<div id="ref-kalampokas2023holistic" class="csl-entry" role="listitem">
+Kalampokas, Theofanis, Konstantinos Tziridis, Nikolaos Kalampokas,
+Alexandros Nikolaou, Eleni Vrochidou, and George A Papakostas. 2023.
+<span>“A Holistic Approach on Airfare Price Prediction Using Machine
+Learning Techniques.”</span> <em>IEEE Access</em> 11: 46627–43.
+</div>
+<div id="ref-sherly2023machine" class="csl-entry" role="listitem">
+Sherly Puspha Annabel, L, G Ramanan, R Prakash, and S Sreenidhi. 2023.
+<span>“Machine Learning-Based Approach for Airfare Forecasting.”</span>
+In <em>Proceedings of International Conference on Data Science and
+Applications: ICDSA 2022, Volume 2</em>, 901–12. Springer.
+</div>
+<div id="ref-sun2024airline" class="csl-entry" role="listitem">
+Sun, Xiaoqian, Changhong Zheng, Sebastian Wandelt, and Anming Zhang.
+2024. <span>“Airline Competition: A Comprehensive Review of Recent
+Research.”</span> <em>Journal of the Air Transport Research
+Society</em>, 100013.
+</div>
+<div id="ref-xiong2023aviation" class="csl-entry" role="listitem">
+Xiong, Xueli, Xiaomeng Song, Anna Kaygorodova, Xichun Ding, Lijia Guo,
+and Jiashun Huang. 2023. <span>“Aviation and Carbon Emissions: Evidence
+from Airport Operations.”</span> <em>Journal of Air Transport
+Management</em> 109: 102383.
+</div>
+<div id="ref-xu2021influential" class="csl-entry" role="listitem">
+Xu, Xu, Clare Anne McGrory, You-Gan Wang, and Jinran Wu. 2021.
+<span>“Influential Factors on Chinese Airlines’ Profitability and
+Forecasting Methods.”</span> <em>Journal of Air Transport
+Management</em> 91: 101969.
+</div>
+</div>