Pre-Owned Car Price Prediction Using Machine Learning Techniques

Andy Zhu

2023

Abstract

As the car industry continues to evolve, car price prediction models have been a highly focused topic for research. In the current pre-owned car marketplace, buyers are impelled to decide whether a vehicle is within a reasonable price range. From visiting car dealers to websites, the task of finding the true worth of a car is laborious, especially for those who have limited knowledge about cars. The rise of online car listing platforms calls for buyers and sellers to be informed about the values of vehicles in the market. A tool that could determine the honest value of a vehicle would be crucial to the automotive marketplace. This paper proposes several machine learning algorithms that aim to predict the prices of used vehicles based on the features of a car. By using linear regression, decision trees, and neural network, this study explores models that best capture the price of used cars from pre-existing car sales data. The models’ performances are assessed to determine the most suitable model for future price prediction applications.

Paper Citation

in Harvard Style

Zhu A. (2023). Pre-Owned Car Price Prediction Using Machine Learning Techniques. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 356-360. DOI: 10.5220/0012810100003885

in Bibtex Style

@conference{daml23,
author={Andy Zhu},
title={Pre-Owned Car Price Prediction Using Machine Learning Techniques},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={356-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012810100003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Pre-Owned Car Price Prediction Using Machine Learning Techniques
SN - 978-989-758-705-4
AU - Zhu A.
PY - 2023
SP - 356
EP - 360
DO - 10.5220/0012810100003885
PB - SciTePress