Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{206861,
        author = {Javeriya Makandar},
        title = {Flight Price Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3106-3116},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206861},
        abstract = {Airfare price prediction has become a central focus in the airline industry, as accurate forecasting directly impacts both travelers and airlines. This study conducts a comparative analysis of several machine learning algorithms—Decision Tree (DT), Random Forest (RF), Logistic Regression, Linear Regression, and XGBoost—applied to flight price prediction.
The proposed system leverages a data-set containing features such as airline name, source, destination, departure and arrival times, duration, number of stops, and journey date. To enhance prediction accuracy, prepossessing techniques including data cleaning, feature extraction, encoding, and normalization are employed. Logistic Regression is also adapted to handle binary classification tasks relevant to tourist ticket outcomes. Model performance is evaluated using the R2 score, and feature importance analysis is conducted to identify key factors influencing airfare pricing. By comparing the strengths and limitations of these algorithms, this study provides insights into the most effective approaches for flight price prediction. The findings aim to assist stakeholders—from travelers to airline operators—in making more informed decisions.},
        keywords = {Flight Delay Prediction Machine Learning Explainable Artificial Intelligence (XAI) Hazrat Shahjalal International Airport Bangladesh Aviation.},
        month = {July},
        }