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- W4377970604 abstract "In the airline sector around the world, flight delay is the biggest issue. In the last 20 years, the expansion of the airline business increased air traffic, which resulted in flight delays. This causes huge losses in airline industry and also negatively impacts the environment. Therefore, it is crucial to avoid delays and cancel flights. In the present work, the flight delay is predicted by using some regression models like Linear Regression and Random Forest Regression, classification models like Logistic Regression and Decision Trees and Sentiment Analysis. Then, based on accuracy, best model will be suggested. The majority of earlier works are only appropriate for one route or airport. This study looks at a variety of airlines as well as variables that could affect flight delays." @default.
- W4377970604 created "2023-05-25" @default.
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- W4377970604 date "2023-01-23" @default.
- W4377970604 modified "2023-09-30" @default.
- W4377970604 title "Predicting Flight Delay Based on Sentimental Analysis: Machine Learning" @default.
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- W4377970604 doi "https://doi.org/10.1109/iccci56745.2023.10128271" @default.
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