Car Price Prediction Using Machine Learning

Rui Chen

2024

Abstract

Car price prediction is always a critical issue for the car industry, with significant implications for consumers, dealers, and manufacturers. This study aims to compare the performance of three machine learning models Linear Regression (LR), Decision Tree (DT), and K-nearest neighbors (KNN) - to predict car prices using a comprehensive dataset of vehicle characteristics. The research integrates diverse methodologies, evaluates model performance using the R-squared (R2) metric, and discusses the implications for practical applications. The DT model, enhanced by feature importance analysis, achieved the highest R2 on the test set, indicating its strong ability to capture complex patterns within the data. These findings underscore the potential of advanced machine learning techniques to provide more accurate and reliable pricing models. By improving price predictions, this research can support stakeholders in developing more effective pricing strategies, ultimately benefiting consumers with fairer prices and helping dealers and manufacturers optimize their revenue and inventory management.

Paper Citation

in Harvard Style

Chen R. (2024). Car Price Prediction Using Machine Learning. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 536-541. DOI: 10.5220/0013270100004568

in Bibtex Style

@conference{ecai24,
author={Rui Chen},
title={Car Price Prediction Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={536-541},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013270100004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Car Price Prediction Using Machine Learning
SN - 978-989-758-726-9
AU - Chen R.
PY - 2024
SP - 536
EP - 541
DO - 10.5220/0013270100004568
PB - SciTePress