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@article{164215,
        author = {N. MARINBI and Pushadapu Lakshmi Narayana and Dhanamsetti Yaswanth and Pula Venkata Kalyan Ram and Shaik Sohel Basha},
        title = {Dynamic Pricing Strategy For Airline Seat Bookings},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {478-482},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164215},
        abstract = {Flight ticket prices can fluctuate greatly depending on factors like the airline you choose, route, time of bookings, and traveling dates. As travelers, we often wonder whether we’re getting a good deal or if prices will change in the near future. To address this uncertainty, we aim to build a ensemble machine learning model that predicts flight ticket prices accurately. We predicting flight ticket prices using machine learning techniques. Our goal is to analyze historical data and draw insights that allow us to calculate the cost of the flights based on different conditions.},
        keywords = {Supervised Learning Algorithm are Decision Tree, Random Forest), Machine Learning, EDA, Root mean square Error.},
        month = {},
        }