DanteSc03/Predicting-Flight-Delays
This project analyzes and visualizes airline delay causes using data sourced from Kaggle. Additionally, it applies machine learning techniques to predict and understand factors contributing to flight delays, such as weather, airline operations, and more.
This project helps airline operations managers and travel analysts understand why flights get delayed. It takes raw airline delay data, processes it, and then visualizes key trends and predicts potential delay causes, providing clear insights into factors like weather or operational issues. The end result is a better understanding of delay patterns and potential improvements in flight scheduling and passenger communication.
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Use this if you need to analyze historical flight delay data to identify common causes and predict future delays for operational planning.
Not ideal if you need real-time flight tracking or a system that directly integrates with live airline operations databases.
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Jupyter Notebook
License
MIT
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Last pushed
Dec 15, 2024
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