Abstract :

Airline Ticket Pricing Represents One Of The Most Complex And Dynamic Pricing Mechanisms In Modern Transportation Systems, Driven By A Combination Of Temporal Variations And Market-oriented Factors. Ticket Fares Fluctuate Continuously Based On Parameters Such As Booking Time, Travel Season, Passenger Demand, Airline Competition, Route Popularity, And Seat Availability. This Inherent Volatility Creates Significant Uncertainty For Travelers, Making It Difficult To Identify The Optimal Time For Booking Tickets At Economical Prices. Consequently, There Is A Growing Need For Intelligent Systems Capable Of Analyzing Historical Trends And Forecasting Future Airfare With A High Degree Of Reliability. This Project Presents A Robust Machine Learning–driven Framework Designed To Predict Airline Ticket Prices By Incorporating Both Temporal Dynamics And Market Behavior Into The Predictive Process. The Proposed System Leverages Structured Historical Flight Data And Applies Advanced Regression Techniques To Uncover Hidden Relationships Between Multiple Influencing Variables. Key Attributes Considered In The Model Include Airline Type, Source And Destination Locations, Departure And Arrival Times, Total Journey Duration, Number Of Stops, And Time-based Features Such As Booking Period And Travel Date. In Addition, Market-related Indicators Such As Route Demand Intensity And Airline Competition Are Integrated To Enhance Predictive Performance. The Framework Follows A Systematic Methodology Involving Data Preprocessing, Feature Engineering, Model Training, And Evaluation. Data Preprocessing Ensures The Removal Of Inconsistencies, Handling Of Missing Values, And Transformation Of Categorical Features Into Numerical Representations. Feature Engineering Plays A Crucial Role In Extracting Meaningful Insights, Particularly From Temporal Variables, Which Significantly Impact Pricing Trends. Multiple Machine Learning Algorithms, Including Linear Regression, Decision Tree Regressor, And Random Forest Regressor, Are Implemented And Compared Using Performance Metrics Such As R² Score And Error Analysis. Among These, Ensemble-based Models Demonstrate Superior Capability In Capturing Non-linear Relationships Within The Dataset.

Published:

22-5-2026

Issue:

Vol. 26 No. 5 (2026)

Page Nos:

1471-1476

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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