Sky Sage: Revolutionizing Airfare Prediction with Advanced Machine Learning Integration
- DOI
- 10.2991/978-94-6463-471-6_121How to use a DOI?
- Keywords
- Aviation Industry; Validation Strategies; Machine Learning; Predictive Model; And Flight Cost Prediction
- Abstract
When choosing a mode of transportation, people today prioritize comfort above all else, favoring air travel above buses and trains. This paper's predictive skills help to provide anticipatory information to match the changing needs of passengers. This study looks into flight cost prediction by identifying designs in the assessment systems of multiple airline companies utilizing automated reasoning techniques. The suggested technique is used on 136,917 Lufthansa, Turkish, Aegean, and Austrian information flights. Carriers are employed to extract many advantageous features for six primary general complaints. To provide precise expected results, this system makes use of machine learning technology by integrating the decision tree regression model, boosting, and bagging algorithms.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - S. Mohan Krishna AU - Ashok Koujalagi AU - B. Kedareswari AU - K. Sandya Reddy AU - M. D. Mahaboob Sharif AU - M. Chandra Raghava Reddy PY - 2024 DA - 2024/07/30 TI - Sky Sage: Revolutionizing Airfare Prediction with Advanced Machine Learning Integration BT - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024) PB - Atlantis Press SP - 1259 EP - 1271 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-471-6_121 DO - 10.2991/978-94-6463-471-6_121 ID - Krishna2024 ER -