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Due to its convenience, luxury, and speed, air travel has become more popular than other modes of transportation. However, because of a number of issues, including erratic weather forecasts, engine problems, and others, there have been major delays as a result of the huge demand for it. These delays cause significant monetary and environmental damage. To enhance flight operations and reduce delays, the model's primary goal is to develop a machine learning model to predict flight delays. Our algorithm would take as inputs the departure date, the separation between the two airports, the scheduled arrival, etc. Additionally, for various figures of merit, we contrast the decision tree classifier with logistic regression and a straightforward neural network. The Random Forest consists of various decision trees that select the suitable attribute for a node starting at the root and separate the data into subsets based on the selected attribute.
Keywords:
Random forest, Predicting the flight delay, Map plotting, Referring near by hotels and transports.
Cite Article:
"Developing a Flight Delay Prediction Model using Machine Learning Algorithms", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.551 - 556, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305086.pdf
Downloads:
000205323
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator