Authors:
Parth Jani, Desiya Nanban, Jennifer Selvan, Nicholas Richardson, R. Sivakani, R. Subhashni
Addresses:
Department of Business Architecture, Tech Operations, Molina Healthcare, Virginia, United States of America, jani.p1212@gmail.com. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India, dn4939@srmist.edu.in, js6485@srmist.edu.in. Department of Software Engineering, JPMorgan Chase, Chicago, Illinois, United States of America, nicrichardson322@gmail.com. Department of Artificial Intelligence and Data Science, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India, sivakani@dhaanishcollege.in. Department of Computer Science and Applications, St. Peter’s Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu, India, subhashniraj2018@gmail.com.
Today, bus services have become a very convenient way of travelling from one place to another in every household. Since the introduction of websites like Redbus.in, bookmytrip.com, etc., which have been incorporated into our lives, booking and travelling on buses has become much easier. However, the price dynamics for these bus services don’t remain constant every single day, and they vary depending on the days as well as the time; for example, if a bus ticket is booked closer to the customer’s departure date, it would be more expensive when compared to the other days before that. Initially, a bus service would set its price for a certain date. Observing this, the other bus services would set a price closer to or lesser than that to attract more customers. So, the main purpose of this study is to find the bus service that initially sets the price. In order to find which bus service initially sets the price, we will use a machine learning algorithm known as k-means clustering, which groups the different bus services into clusters with similar data fields.
Keywords: Machine Learning (ML); K-means Clustering; Feature Engineering (FE); Bus Services; Price Dynamics (PD); Price Category; Machine Learning Tools (MLT); Price Dynamics in Online.
Received on: 03/09/2023, Revised on: 05/11/2023, Accepted on: 07/01/2024, Published on: 05/03/2024
AVE Trends in Intelligent Computing Systems, 2024 Vol. 1 No. 1, Pages: 54-65