A framework for predicting airfare prices using machine learning
Joint Authors
Fadil, Hibah Muhammad
Abd Allah, Muhammad Najm
Yunus, Muhammad Isam
Source
Iraqi Journal of Computer, Communications and Control Engineering
Issue
Vol. 22, Issue 3 (30 Sep. 2022), pp.81-96, 16 p.
Publisher
Publication Date
2022-09-30
Country of Publication
Iraq
No. of Pages
16
Main Subjects
Telecommunications Engineering
Information Technology and Computer Science
Abstract EN
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better.
A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy.
Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create.
Hence, the proposed methodologies were used to predict flight prices.
A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), Knearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation.
These approaches were tested on a data set of an extensive Indian airline network.
When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy.
The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively.
This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion
American Psychological Association (APA)
Fadil, Hibah Muhammad& Abd Allah, Muhammad Najm& Yunus, Muhammad Isam. 2022. A framework for predicting airfare prices using machine learning. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 22, no. 3, pp.81-96.
https://search.emarefa.net/detail/BIM-1492787
Modern Language Association (MLA)
Fadil, Hibah Muhammad…[et al.]. A framework for predicting airfare prices using machine learning. Iraqi Journal of Computer, Communications and Control Engineering Vol. 22, no. 3 (Sep. 2022), pp.81-96.
https://search.emarefa.net/detail/BIM-1492787
American Medical Association (AMA)
Fadil, Hibah Muhammad& Abd Allah, Muhammad Najm& Yunus, Muhammad Isam. A framework for predicting airfare prices using machine learning. Iraqi Journal of Computer, Communications and Control Engineering. 2022. Vol. 22, no. 3, pp.81-96.
https://search.emarefa.net/detail/BIM-1492787
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 94-96
Record ID
BIM-1492787