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Flight Price Prediction Using Machine Learning Techniques

B.S. Panda1 , B. Phanendra Varma2 , B. Chandini3 , R. Bhoomika4

Section:Research Paper, Product Type: Journal Paper
Volume-10 , Issue-9 , Page no. 10-13, Sep-2022

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v10i9.1013

Online published on Sep 30, 2022

Copyright © B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika, “Flight Price Prediction Using Machine Learning Techniques,” International Journal of Computer Sciences and Engineering, Vol.10, Issue.9, pp.10-13, 2022.

MLA Style Citation: B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika "Flight Price Prediction Using Machine Learning Techniques." International Journal of Computer Sciences and Engineering 10.9 (2022): 10-13.

APA Style Citation: B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika, (2022). Flight Price Prediction Using Machine Learning Techniques. International Journal of Computer Sciences and Engineering, 10(9), 10-13.

BibTex Style Citation:
@article{Panda_2022,
author = {B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika},
title = {Flight Price Prediction Using Machine Learning Techniques},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2022},
volume = {10},
Issue = {9},
month = {9},
year = {2022},
issn = {2347-2693},
pages = {10-13},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5517},
doi = {https://doi.org/10.26438/ijcse/v10i9.1013}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v10i9.1013}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5517
TI - Flight Price Prediction Using Machine Learning Techniques
T2 - International Journal of Computer Sciences and Engineering
AU - B.S. Panda, B. Phanendra Varma, B. Chandini, R. Bhoomika
PY - 2022
DA - 2022/09/30
PB - IJCSE, Indore, INDIA
SP - 10-13
IS - 9
VL - 10
SN - 2347-2693
ER -

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Abstract

This article will examine the issue of foreseeing air passages. To do this, a great deal of things has been distinguished, and you believe that the qualities of a typical airplane will influence the cost of aircraft tickets. Highlights are utilized in eight current AI strategies, used to foresee airplane costs, and model execution is thought about. As well as cautiously anticipating each model, this paper cautiously inspects the data used to distinguish carrier tickets.

Key-Words / Index Term

Machine Learning, Decision tree, Random Forest, K-Nearest Method.

References

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[2] Moira McCormick, Black Curce, “Behind the Scenes of Airline Pricing Strategies”, September 19, 2017.
[3] K. Tziridis, Th. Kalampokas, G. A. Papakostas, “Airfare Prices Prediction Using Machine Learning Techniques”, 25th European Signal Processing Conference (EUSIPCO), IEEE, October 26, 2017.
[4] Tianyi Wang, Samira Pouyanfar, Haiman Tian, Yudong Tao, Miguel Alonso Jr., Steven Luis and Shu-Ching Chen, “A Framework for Airfare Price Prediction: A Machine Learning Approach”, 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), September 9, 2019.
[5] Tao Liu, Jian Cao, Yudong Tan, Quanwu Xiao, “ACER: An Adaptive Context- Aware Ensemble Regression Model for Airfare Price Prediction”, 2017 International Conference on Progress in Informatics and Computing (PIC), December, 2017.
[6] Supriya Rajankar, Neha Sakharkar, Omprakash Rajankar, “Predicting the price of a flight ticket with the use of Machine Learning algorithms”, international journal of scientific & technology research, vol.8, December, 2019.
[7] Juhar Ahmed Abdella, Nazar Zaki and Khaled Shuaib, “Automatic Detection of Airline Ticket Price and Demand: A Review”, 13th International Conference on Innovations in Information technology (IIT), January 10, 2019.
[8] Chaya Bakshi, Medium, “Random Forest Regression”, June 9, 2020.
[9] Zach, Statology, “How to calculate mean Absolute Error in Python”, January 8. 2021.
[10] Bickel, Peter J.; Doksum, Kjell A. Mathematical Statistics: Basic Ideas and Selected Topics. Vol. I (Second ed.). p. 20. 2015.
[11] Tianfeng Chai, R. R. Draxler, “Root-Mean Squared Error”, Geoscientific Model Development Discussions 7(1), DOI: 10.5194/gmdd-7-1525-2014.