Abstract: Flight ticket prices fluctuate based on factors such as flight timing, destination, and duration. To address the challenge of determining the optimal time to purchase tickets, this proposed system aims to develop a predictive model using machine learning algorithms. By analyzing historical flight data, our project focuses on identifying underlying price trends in India and providing recommendations for the best time to buy tickets.This project seeks to validate or debunk myths surrounding the airline industry, comparing different models to predict the optimal timing for ticket purchases and potential cost savings. Notably, price trends vary significantly depending on the route, month, day, time of departure, whether it's a holiday, and the airline carrier. For highly competitive routes like major business destinations (e.g., Mumbai-Delhi), prices tend to increase as the departure date approaches. However, other routes, such as tier 1 to tier 2 cities like Delhi-Guwahati, have specific time frames when prices are at their lowest. Additionally, the collected data reveals two distinct categories of airline carriers in India: the economical group and the luxurious group. In most cases, the lowest-priced flights belong to the economical group. Furthermore, the data confirms that certain periods of the day are associated with higher expected prices. Expanding the scope of this project to cover various routes can lead to significant savings when purchasing domestic flight tickets in the Indian market..
Keywords: Flight ticket, Optimal timing, historical data, competitive routes, Indian Domestic Airline market
|
DOI:
10.17148/IJARCCE.2023.125140
