Open AccessFlight Ticket Price Prediction Using Regression Models
Author(s) -
S. Manoj Krishna,
G. Sharitha,
P. Madhu Ganesh,
G.V. Ajith Kumar,
G. Karthika
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.41058
Subject(s) - ticket , computer science , regression analysis , regression , predictive modelling , econometrics , operations research , machine learning , economics , statistics , engineering , mathematics , computer security
Abstract: Many people nowadays choose to travel by flights. The cost of an airline ticket has a significant impact on a traveller’s decision on which mode of transportation to use. A wide number of factors influence the price of an airline ticket, including social, competitive, marketing, and financial factors, among others. Every airline has a different technique for determining ticket prices. We can uncover the rules that airlines may use to model their fare variation using Machine Learning. In this project paper, we propose developing a web-based application for projecting the price of a flight ticket using Kaggle data, where the dataset contains various data related to 10,000 flights. The framework proposed will be used to simulate several regression algorithms for estimating projected flight fares. The model that will produce extremely accurate forecasts will be finalized, and it will solely be utilised to forecast the price. Keywords: regression, machine learning, model, prediction, algorithms