Pre-Owned Bike Price Prediction Using Machine Learning

Sathyanarayanan, Keerthana (2023) Pre-Owned Bike Price Prediction Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Middle class is the economic and social backbone of India. They form a substantial portion of India’s population. Due to the cost-effectiveness and affordability, pre-owned bikes market has attracted the middle class. Not only for the reason of affordability, pre-owned bikes are becoming more popular in India for a number of reasons.The focus is also placed on the requirement for pre-owned bikes for both personal and business transportation or commercial purposes. Due to this surge, it is crucial to ensure fair pricing of pre-owned bikes that benefits both buyers and sellers. Many studies concentrate on determining the values of secondhand cars and automobiles, but there is not much research on predicting the price of pre-owned bikes. Thus, this project explores the use of machine learning models for predicting the prices of used bikes and comprehensive comparative analysis is performed. The study evaluates five classic machine learning models, including Linear Regression, Elastic Regression, Support Vector Regressor, Random Forest, and XG Boost, to identify the best models to provide sellers and buyers with fair pricing. The findings of the study include two top performing models, Random Forest and XG Boost.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Trinh, Anh Duong UNSPECIFIED
Uncontrolled Keywords:	Pre-Owned Bikes; Machine Learning Models; Price prediction; Comparative Analysis
Subjects:	H Social Sciences > HF Commerce Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	02 Jan 2025 13:50
Last Modified:	02 Jan 2025 13:50
URI:	https://norma.ncirl.ie/id/eprint/7264

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