COMPARATIVE ANALYSIS OF LINEAR REGRESSION AND RANDOM FOREST FOR USED CAR PRICE PREDICTION
Abstract
Manual estimation is often subjective and prone to human bias because the used car market has a complex pricing structure with non-linear depreciation. Objective: This study conducted a comparative analysis between Linear Regression and Random Forest algorithms to develop a more objective pricing model. Methods: The Kaggle dataset contains 5,000 entries indicating features such as manufacturer, model, engine size, and mileage for this study. The methodology included data cleaning, feature engineering, and outlier removal using the IQR method. For training and testing, the data was split 80:20. Results: "Year of Manufacture" was identified as the feature that most significantly influences price, and the evaluation results showed a significant difference in performance. Linear Regression achieved 82.33% accuracy, while Random Forest achieved 99.60% accuracy. Conclusion: Random Forest captures non-linear patterns and complex relationships in used car pricing better than Linear Regression, although it remains quite reliable for general trends.
Keywords
References
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DOI: http://dx.doi.org/10.22441/collabits.v3i1.37646
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