Summary of Flight Delay Prediction Using Hybrid Machine Learning Approach: a Case Study Of Major Airlines in the United States, by Rajesh Kumar Jha et al.
Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
by Rajesh Kumar Jha, Shashi Bhushan Jha, Vijay Pandey, Radu F. Babiceanu
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed hybrid approach combines deep learning and classic machine learning techniques to address the flight delay problem in the aviation industry. By applying various machine learning algorithms on flight data, the study validates the performance of the proposed model using metrics such as accuracy, precision, recall, F1-score, ROC curves, and AUC curves. The results provide insightful recommendations for U.S. airlines to mitigate flight delays. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs called machine learning models to help solve a big problem in air travel: flight delays. When planes are late, it wastes fuel, labor, and money. As the number of flights keeps growing, this issue will only get worse. The researchers mixed two types of computer models together to try and find a solution. They tested these models on real flight data and used special tools like accuracy scores and graphs to see how well they worked. The results can help airlines make better decisions to keep flights running on time. |
Keywords
» Artificial intelligence » Auc » Deep learning » F1 score » Machine learning » Precision » Recall
