| Autor: |
Samanvitha M, B. V. Kiranmayee, Jeshmitha G, Mahesh J, N Lakshmi Kalyani, Bindu Sri Sai U |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). |
| DOI: |
10.1109/i-smac49090.2020.9243339 |
| Popis: |
Prior prediction of flight arrival delays is necessary for both travelers and airlines because delays in flights not only trigger huge economic loss but also airlines end up losing their reputation that was built for several years and passengers lose their valuable time. Our paper aims at predicting the arrival delay of a scheduledindividual flight at the destination airport by utilizing available data. The predictive model presented in this work is to foresee airline arrival delays by employing supervised machine learning algorithms. US domestic flight data along with the weather data from July 2019 to December 2019 were acquired and are used while training the predictive model. XGBoost and linear regression algorithms were applied to develop the predictive model that aims at predicting flight delays. The performance of each algorithm was analyzed. Flight data along with the weather data was given to the model. Using this data, binary classification was carried out by the XGBoost trained model to predict whether there would be any arrival delay or not, and then linear regression model predicts the delay time of the flight. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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