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  Published Paper Details:

  Paper Title

An Accurate Prediction of Used Car Price Using XGBoost Regressor in Comparison with Random Forest and Decision Tree Regressor

  Authors

  Prateek Kumar,  Shiromani Kumar,  Shivam Gupta,  Santhosh Kumar C

  Keywords

Random Forest, Decision Tree, XGBoost Regressor, Machine Learning, Car Price Prediction.

  Abstract


To predict the price of second-hand cars, we have implemented three approaches namely, XGBoost, Random Forest, and Decision Tree Regressors. The Kaggle sourced dataset is cleaned, feature selected and treated for outliers in order to improve the accuracy. To evaluate the performance of the model and for knowing which factors in the price affect, we use the R2 score and Metrics evaluation of the Scikit learn module available in Python. Therefore, XGBoost is likely to outperform the rest because it is more efficient. Thus, we build a simple to use web application where Users may input Automobile details and get the same time price estimates. Our project serves to help buyers and sellers use the data to accurately predict a second-hand car price.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTBE02119

  Paper ID - 289366

  Page Number(s) - 925-931

  Pubished in - Volume 13 | Issue 7 | July 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Prateek Kumar,  Shiromani Kumar,  Shivam Gupta,  Santhosh Kumar C,   "An Accurate Prediction of Used Car Price Using XGBoost Regressor in Comparison with Random Forest and Decision Tree Regressor", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 7, pp.925-931, July 2025, Available at :http://www.ijcrt.org/papers/IJCRTBE02119.pdf

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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
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