IDEAS home Printed from https://ideas.repec.org/a/dbk/rlatia/v2y2024ip93id1062486latia202493.html
   My bibliography  Save this article

Prediction of Flight Areas using Machine Learning Algorithm

Author

Listed:
  • Khushwant Singh
  • Mohit Yadav
  • Vugar Hacimahmud Abdullayev

Abstract

Anyone who often uses the airways wants to predict when it will be best to purchase a ticket in order to get the best possible value. Aircraft firms continuously adjust ticket prices in an effort to maximize profits. When it's anticipated that demand for more income will grow, aircraft manufacturers may raise flying prices. Information analysis for a given air route, comprising the features like take-off time, entrance time, and airways during a specified period, has been gathered in order to decrease costs. To use the machine learning models, qualities are arranged based on the information that has been gathered. The machine learning approach to determine costs based on attributes is presented in the paper below.

Suggested Citation

Handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:93:id:1062486latia202493
DOI: 10.62486/latia202493
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dbk:rlatia:v:2:y:2024:i::p:93:id:1062486latia202493. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Javier Gonzalez-Argote (email available below). General contact details of provider: https://latia.ageditor.uy/ .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.