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November 3, 2025Open Access

An Empirical Study on Used Car Price Prediction Using Supervised and Unsupervised Learning

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Empirical study demonstrates effective price prediction using supervised and unsupervised learning, highlighting key features like horsepower.

XGBoost achieved an R² of 0.87 in predicting used car prices, indicating effective model performance.
The study utilized Principal Component Analysis to maintain 95% variance while reducing model dimensionality.
Comparative analysis of machine learning models included Random Forest, Elastic Net regression, and Support Vector Machines.
Key insights were derived from using unsupervised learning techniques like hierarchical clustering for vehicle segmentation.

Rao et al. (2025) studied this question.

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November 3, 2025Open Access

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Authors

Empirical study demonstrates effective price prediction using supervised and unsupervised learning, highlighting key features like horsepower.

XGBoost achieved an R² of 0.87 in predicting used car prices, indicating effective model performance.
The study utilized Principal Component Analysis to maintain 95% variance while reducing model dimensionality.
Comparative analysis of machine learning models included Random Forest, Elastic Net regression, and Support Vector Machines.
Key insights were derived from using unsupervised learning techniques like hierarchical clustering for vehicle segmentation.

Rao et al. (2025) studied this question.

View Full Paper

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