The Integration of Machine Learning Technique with the Existing System to Predict the Flight Prices-Reference-Cited by-同舟云学术

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The Integration of Machine Learning Technique with the Existing System to Predict the Flight Prices

Published:2023-05-12 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)
language:
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Author:

Mary Joshitta Shantha¹,M P Sunil²,BadriaSulaimanAlfurhood ³,Bodhankar Aniruddha⁴,Ch.Sreedevi ⁵,Khanna Rupa⁶

Affiliation:

1. Affiliated to Mother Teresa Women's University,JayarajAnnapackiam College for Women (Autonomous), Periyakulam,Kodaikanal,Tamilnadu,India

2. JAIN (Deemed-to-be University),Department of ECE,Bangalore,India

3. Princess Nourahbint Abdulrahman University,College of Computer Information Sciences,Department of Computer Sciences,Saudi Arabia

4. University : Dr.Ambedkar Institute of Management Studies & Research,Department : Management,Nagpur

5. CSE B V Raju Institute Of Technology,Narsapur,Telangana,India

6. Graphic Era Deemed To Be University,Department Of Commerce,Dehradun,Uttarakhand,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10182388/10182370/10182539.pdf?arnumber=10182539

Reference16 articles.

1. Flight passenger load factors prediction based on RNN using multi-granularity time attention;yujing;Computer Engineering,2020

2. Airline Price Prediction using Machine Learning;shukla,0

3. Dynamic price dispersion in airline markets

4. New York City taxi trip duration prediction using MLP and XGBoost

5. Does Competition Reduce Price Dispersion? New Evidence from the Airline Industry

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