FLIGHT DELAY PREDICTION USING MACHINE LEARNING
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901745959960576 |
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| author | D V Phanindra Kumar, Dr. N. P. Lavanya Kumari |
| author_facet | D V Phanindra Kumar, Dr. N. P. Lavanya Kumari |
| contents | <p><a href="https://ijetrm.com/issues/files/Jul-2025-30-1753885931-JULY135.pdf" target="_blank" rel="noopener">Flight delays </a>are a persistent issue in the aviation industry, affecting passenger satisfaction, airline operations,<br>and airport efficiency. These delays can be caused by various factors such as weather conditions, technical<br>issues, air traffic congestion, or crew unavailability. Unpredictable delays not only inconvenience travelers but<br>also lead to significant financial losses for airlines and logistical disruptions across the network. As the volume<br>of air traffic continues to grow, there is an urgent need for systems that can forecast potential delays accurately<br>and in advance. This project proposes a machine learning-based flight delay prediction system that leverages<br>historical flight data along with additional features such as weather reports, flight schedules, and airport traffic<br>information. Multiple machine learning algorithms—including Random Forest, Decision Tree, and XGBoost—<br>were trained and evaluated to determine the most effective model for predicting delays. Data preprocessing<br>techniques such as feature selection, normalization, and label encoding were applied to ensure data quality and<br>model performance. The model predicts whether a given flight is likely to be delayed, helping airlines and<br>passengers plan accordingly. The results demonstrate that machine learning can significantly enhance the<br>accuracy of delay predictions compared to traditional rule-based systems. By integrating predictive analytics<br>into airline operations, the system can aid in resource allocation, improve passenger communication, and reduce<br>cascading delays across routes. This approach not only offers a practical solution to a real-world problem but<br>also highlights the potential of artificial intelligence in optimizing air travel operations.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16614246 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | FLIGHT DELAY PREDICTION USING MACHINE LEARNING D V Phanindra Kumar, Dr. N. P. Lavanya Kumari <p><a href="https://ijetrm.com/issues/files/Jul-2025-30-1753885931-JULY135.pdf" target="_blank" rel="noopener">Flight delays </a>are a persistent issue in the aviation industry, affecting passenger satisfaction, airline operations,<br>and airport efficiency. These delays can be caused by various factors such as weather conditions, technical<br>issues, air traffic congestion, or crew unavailability. Unpredictable delays not only inconvenience travelers but<br>also lead to significant financial losses for airlines and logistical disruptions across the network. As the volume<br>of air traffic continues to grow, there is an urgent need for systems that can forecast potential delays accurately<br>and in advance. This project proposes a machine learning-based flight delay prediction system that leverages<br>historical flight data along with additional features such as weather reports, flight schedules, and airport traffic<br>information. Multiple machine learning algorithms—including Random Forest, Decision Tree, and XGBoost—<br>were trained and evaluated to determine the most effective model for predicting delays. Data preprocessing<br>techniques such as feature selection, normalization, and label encoding were applied to ensure data quality and<br>model performance. The model predicts whether a given flight is likely to be delayed, helping airlines and<br>passengers plan accordingly. The results demonstrate that machine learning can significantly enhance the<br>accuracy of delay predictions compared to traditional rule-based systems. By integrating predictive analytics<br>into airline operations, the system can aid in resource allocation, improve passenger communication, and reduce<br>cascading delays across routes. This approach not only offers a practical solution to a real-world problem but<br>also highlights the potential of artificial intelligence in optimizing air travel operations.</p> |
| title | FLIGHT DELAY PREDICTION USING MACHINE LEARNING |
| url | https://doi.org/10.5281/zenodo.16614246 |