Machine Learning Optimization and Challenges in Used Car Price Prediction
With the rapid expansion of the second-hand vehicle market, correctly forecasting car prices is essential for both researchers and industry experts. The paper initially reviews existing machine learning models and their performance in predicting luxury car prices, emphasizing both their strengths an...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04032.pdf |
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| _version_ | 1825206567487668224 |
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| author | Zheng Yufan |
| author_facet | Zheng Yufan |
| author_sort | Zheng Yufan |
| collection | DOAJ |
| description | With the rapid expansion of the second-hand vehicle market, correctly forecasting car prices is essential for both researchers and industry experts. The paper initially reviews existing machine learning models and their performance in predicting luxury car prices, emphasizing both their strengths and limitations. To begin with, models like XGBoost and Random Forest excel at processing large-scale data and identifying complex feature patterns, thanks to their ability to use an ensemble of decision trees to reduce bias and variance. However, these models struggle to accurately capture the unique characteristics of luxury vehicles, such as brand reputation, rarity, and personalized configurations. Because these complex factors cannot be easily represented by simple numerical features, the result is often suboptimal predictions for high-value vehicle prices. The paper found that feature engineering could enhance model performance by introducing more representative attributes specific to luxury vehicles, such as brand reputation, rarity, and customization options. Additionally, stratified modeling, which segments data based on price tiers, may provide more accurate predictions by targeting different price levels, especially in the high-value vehicle segment. Despite these theoretical benefits, the paper acknowledges that while these strategies were discussed, they were not empirically tested in detail. Consequently, their practical effectiveness still requires further investigation. |
| format | Article |
| id | doaj-art-6a6694c989e84fa892183ce388010228 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-6a6694c989e84fa892183ce3880102282025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700403210.1051/itmconf/20257004032itmconf_dai2024_04032Machine Learning Optimization and Challenges in Used Car Price PredictionZheng Yufan0Santa Monica CollegeWith the rapid expansion of the second-hand vehicle market, correctly forecasting car prices is essential for both researchers and industry experts. The paper initially reviews existing machine learning models and their performance in predicting luxury car prices, emphasizing both their strengths and limitations. To begin with, models like XGBoost and Random Forest excel at processing large-scale data and identifying complex feature patterns, thanks to their ability to use an ensemble of decision trees to reduce bias and variance. However, these models struggle to accurately capture the unique characteristics of luxury vehicles, such as brand reputation, rarity, and personalized configurations. Because these complex factors cannot be easily represented by simple numerical features, the result is often suboptimal predictions for high-value vehicle prices. The paper found that feature engineering could enhance model performance by introducing more representative attributes specific to luxury vehicles, such as brand reputation, rarity, and customization options. Additionally, stratified modeling, which segments data based on price tiers, may provide more accurate predictions by targeting different price levels, especially in the high-value vehicle segment. Despite these theoretical benefits, the paper acknowledges that while these strategies were discussed, they were not empirically tested in detail. Consequently, their practical effectiveness still requires further investigation.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04032.pdf |
| spellingShingle | Zheng Yufan Machine Learning Optimization and Challenges in Used Car Price Prediction ITM Web of Conferences |
| title | Machine Learning Optimization and Challenges in Used Car Price Prediction |
| title_full | Machine Learning Optimization and Challenges in Used Car Price Prediction |
| title_fullStr | Machine Learning Optimization and Challenges in Used Car Price Prediction |
| title_full_unstemmed | Machine Learning Optimization and Challenges in Used Car Price Prediction |
| title_short | Machine Learning Optimization and Challenges in Used Car Price Prediction |
| title_sort | machine learning optimization and challenges in used car price prediction |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04032.pdf |
| work_keys_str_mv | AT zhengyufan machinelearningoptimizationandchallengesinusedcarpriceprediction |