Predicting Airline Ticket Prices Using Machine Learning

Predicting Airline Ticket Prices Using Machine Learning

Dr.N.Gopala Krishna^*

Gopu Iswarya¹, Bhavirisetty Sakunthala Narasimharao²,

Bhahunandham Naga Durga Devi³ , Bhupathi Dani⁴ ,

^1,2,3,4 Student, Department of Computer Science and Engineering, Tirumala Engineering College

^* Professor, Department of Computer Science and Engineering, Tirumala Engineering College

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Abstract - Airfare prices are constantly changing due to various factors like the time of flight, destination, and length of the trip. To help people figure out the best time to buy tickets, we are working on a system that will use machine learning to predict prices. By studying past flight data in India, we want to uncover pricing patterns and suggest the ideal time to purchase tickets. This project aims to confirm or dispel common beliefs about airlines by comparing prediction models and finding ways to save money on ticket purchases. Price trends can vary significantly based on various factors such as the route, month, day, time of departure, holidays, and the airline carrier. Competitive routes like major business destinations (Mumbai-Delhi) tend to see price increases closer to the departure date. On the other hand, routes like tier 1 to tier 2 cities (Delhi-Guwahati) have specific time frames when prices are lower. The data also shows that there are two main categories of airline carriers in India: economical and luxurious. Generally, the lowest-priced flights fall under the economical group. Moreover, the data confirms that specific times of the day tend to have higher prices. By including different routes in this project, there is a potential for substantial savings when buying domestic flight tickets in India.

Key Words:  Airline Ticket, Pricing strategies, Fare fluctuations, Competitor analysis, Market trends