Airline Recommendation System

Authors

  • Y. Durga Tejaswi, Nagarajupalli Chenchu Gowri, Chittoor Manjunath, Cherlopalli Hemalatha Shaik Rizvana

Keywords:

Feature Extraction, Flight Fare Prediction, Real- Time Detection, Supervised Learning, Predictive Analytics, Machine Learning, natural language processing

Abstract

Airline Recommendation System is a flight fare prediction application utilizing machine learning techniques to estimate the prices of air tickets based on source, destination, number of stops, and airlines. The system has used comprehensive data preprocessing, such as cleaning, wrangling, and exploratory data analysis, to extract meaningful insights. A Random Forest Regressor model has been applied to frame the problem as a regression task so that the correct fare can be predicted. Beyond fare prediction, the application also now offers an updated feature on sentiment analysis of airline reviews. This causes the application to give clients bits of knowledge into traveler encounters as well as by and large carrier administration quality. This improvement enables travelers to make informed decisions by including both pricing and the feedback of other customers. The interactive platform offers real-time fare estimates and airline recommendations based on sentiment from an efficient web-based interface developed in Flask.

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References

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Published

19.12.2024

How to Cite

Y. Durga Tejaswi. (2024). Airline Recommendation System. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5114–5120. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7286

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

License

Creative Commons License

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