Open AccessAircraft Ticket Price prediction using Machine Learning
Author(s) -
Janhvi Mukane,
Siddharth Pawar,
S. U. Pawar,
Gaurav Muley
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.2022.40253
Subject(s) - random forest , computer science , ticket , artificial intelligence , machine learning , analytics , task (project management) , air travel , set (abstract data type) , constant (computer programming) , regression , deep learning , aviation , data mining , statistics , engineering , mathematics , computer security , systems engineering , programming language , aerospace engineering
With ever increasing air route connectivity throughout the world, air travel has become a common, integral and faster way to travel. Predicting fares for airlines is an important as well as challenging task since a constant fluctuation in fares is observed and it is known to be dependent on varied set factors. With tremendous study in area, it is observed that using Machine Learning, Artificial Intelligence and Deep Learning techniques an estimation of flight fares at a given time can be obtained within seconds. In this paper, we use a Machine Learning Regression approach to predict flight fare by providing basic details of departure date and time, arrival time, source, destination, number of stops and name of the airline. The results show that Random Forest Regression Model provides highly optimal results. Keywords: Machine Learning, Random Forest, Prediction models, Airfare Prices, data analytics