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The Research on Prediction of Used Car Prices using Regression Model and LightGBM Model
- 1 Shenzhen Hong Kong Pui Kiu College Longhua Xin Yi School, Shenzhen, Guangdong Province 518131, China
* Author to whom correspondence should be addressed.
Abstract
As the car industry has been developed rapidly, the used car market has also shown its potential due to the affordability and ability to make prediction on prices. This study aims to predict the prices of used car by using two methods: Linear regression model and LightGBM model and make comparison on the performance of two models. The dataset used are found in Kaggle which contain 5847 groups of data with 11 different variables in total affecting the price. In this paper, only 10 variables are chosen to process in the two models and evaluate the results. It has been found that LightGBM model are better than linear regression model with a higher efficiency, suitability and accuracy, with an R2 value of 0.962 for the training set and 0.930 for the test set, compared to Linear Regression's R2value of 0.700. Additionally, LightGBM demonstrates lower prediction errors (MAE: 1.103, MSE: 4.858, RMSE: 2.204) and better handling of large-scale data. To conclude, the LightGBM model has higher accuracy and is more suitable to predict the used car prices with higher efficiency especially when processing complex, large-scale data compared to linear regression model.
Keywords
Prediction, used car prices, machine learning
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Cite this article
QIAN,D. (2025). The Research on Prediction of Used Car Prices using Regression Model and LightGBM Model. Theoretical and Natural Science,105,80-88.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Mathematical Physics and Computational Simulation
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