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Airlines Ticket Price Prediction Using Machine learning approach

Kusam Bhargavi1 , A. Lahari Sai2 , K. Thirupathi Rao3

Section:Research Paper, Product Type: Journal Paper
Volume-10 , Issue-2 , Page no. 49-53, Feb-2022

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v10i2.4953

Online published on Feb 28, 2022

Copyright © Kusam Bhargavi, A. Lahari Sai, K. Thirupathi Rao . 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: Kusam Bhargavi, A. Lahari Sai, K. Thirupathi Rao, “Airlines Ticket Price Prediction Using Machine learning approach,” International Journal of Computer Sciences and Engineering, Vol.10, Issue.2, pp.49-53, 2022.

MLA Style Citation: Kusam Bhargavi, A. Lahari Sai, K. Thirupathi Rao "Airlines Ticket Price Prediction Using Machine learning approach." International Journal of Computer Sciences and Engineering 10.2 (2022): 49-53.

APA Style Citation: Kusam Bhargavi, A. Lahari Sai, K. Thirupathi Rao, (2022). Airlines Ticket Price Prediction Using Machine learning approach. International Journal of Computer Sciences and Engineering, 10(2), 49-53.

BibTex Style Citation:
@article{Bhargavi_2022,
author = {Kusam Bhargavi, A. Lahari Sai, K. Thirupathi Rao},
title = {Airlines Ticket Price Prediction Using Machine learning approach},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2022},
volume = {10},
Issue = {2},
month = {2},
year = {2022},
issn = {2347-2693},
pages = {49-53},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5447},
doi = {https://doi.org/10.26438/ijcse/v10i2.4953}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v10i2.4953}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5447
TI - Airlines Ticket Price Prediction Using Machine learning approach
T2 - International Journal of Computer Sciences and Engineering
AU - Kusam Bhargavi, A. Lahari Sai, K. Thirupathi Rao
PY - 2022
DA - 2022/02/28
PB - IJCSE, Indore, INDIA
SP - 49-53
IS - 2
VL - 10
SN - 2347-2693
ER -

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Abstract

The main theme of this paper deals with the prediction of airlines prices as they may vary many times based on different constraints or attributes of the dataset which affect the fare of airlines. There are many ML(machine learning) models which help us to predict mainly two types of customer side models. The main focus of them is to provide the optimal time to buy a ticket and the fare of tickets should be as cheap as possible. We have considered a dataset which is consisting of ten thousand six hundred and eighty-three entries and eleven columns or attributes. For this paper, we have plotted dist plots and used some of the algorithms/methods to get the accuracy. This project helps the customers/buyers to tell the best time to purchase the ticket and that too with the lowest price. Out of all the methods used, We found random forest gives the most accuracy than the other models.

Key-Words / Index Term

Python,Pandas,ml: prediction models

References

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