Dynamic Pricing Strategy For Airline Seat Bookings

N. MARINBI; Pushadapu Lakshmi Narayana; Dhanamsetti Yaswanth; Pula Venkata Kalyan Ram; Shaik Sohel Basha

Dynamic Pricing Strategy For Airline Seat Bookings

Authors: N. MARINBI, Pushadapu Lakshmi Narayana, Dhanamsetti Yaswanth, Pula Venkata Kalyan Ram, Shaik Sohel Basha

Unique Paper ID: 164215
Volume: 10
Issue: 12
PageNo: 478-482

Keywords: Supervised Learning Algorithm are Decision Tree Random Forest) Machine Learning EDA Root mean square Error.

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 weâ€™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.

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Cite This Article

ISSN: 2349-6002
Volume: 10
Issue: 12
PageNo: 478-482

Dynamic Pricing Strategy For Airline Seat Bookings

Available:https://ijirt.org/Article?manuscript=164215

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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