Abstract :

The Prediction Of Used Car Prices Has Become An Important Problem In The Automotive Industry Due To The Rapid Growth Of Online Car Marketplaces And Increasing Demand For Second-hand Vehicles. Buyers And Sellers Often Face Challenges In Determining The Fair Value Of A Used Car Because Prices Depend On Multiple Factors Such As Age, Mileage, Brand, Fuel Type, And Market Trends. Machine Learning Techniques Have Emerged As Effective Tools To Analyze These Factors And Provide Accurate Price Predictions. These Models Help Reduce Information Asymmetry And Improve Decision-making For Both Buyers And Sellers.Recent Studies Highlight The Effectiveness Of Regression Models, Ensemble Methods, And Deep Learning Approaches In Predicting Used Car Prices. Techniques Such As Random Forest, XGBoost, And Multiple Linear Regression Have Shown High Accuracy By Capturing Both Linear And Non-linear Relationships Between Features And Price. The Integration Of Data-driven Approaches Enables The Development Of Intelligent Pricing Systems That Enhance Transparency And Efficiency In The Used Car Market

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

41 - 45

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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