Smart Used Vehicle Price Prediction And Market System

Abstract

The Used Vehicle Market Has Experi- Enced Significant Growth In Recent Years, Making It Challenging For Buyers And Sellers To Determine Accurate Vehicle Prices. Traditional Pricing Meth- Ods Often Rely On Personal Judgment, Which May Lead To Inconsistencies And Unfair Transactions [12]. In This Work, A Smart Web-based System Is Developed To Predict The Price Of Used Vehicles Using Machine Learning Techniques. The System Employs A Random Forest Regression Model To Analyze Key Features Such As Mileage, Brand, And Engine Capacity To Estimate A Fair Resale Value [5]. In Addition To Price Prediction, The Platform Provides Features Such As Vehicle Recommenda- Tions, Price Trend Analysis, And Location-based Search, Enhancing User Experience And Decision- Making [13]. Experimental Results Demonstrate That The Proposed Model Achieves High Accuracy And Is Suitable For Real-world Applications [14].

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