ACCURATE PRICE PREDICTION OF USED CARS USING MACHINE LEARNING MODELS

A car price prediction has been a highinterest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars. We applied machine learning (linear regression , logistic regression, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble and regressions. The data used for the prediction was collected Respective performances of different algorithms were then compared to find one that best suits the available data set. With the help of algorithms we have to predict the best percentage and also reveal the output with respective algorithms.

Authors

Publication Details

Journal
International Journal of Engineering Research and Science & Technology
Published
2025-04-26
DOI
https://doi.org/10.62643/ijerst.2025.v21.i2.pp1043-1049
Citations
1
Primary Topic
Energy, Environment, and Transportation Policies
Type
article
Field-Weighted Citation Impact
0.36
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article

ACCURATE PRICE PREDICTION OF USED CARS USING MACHINE LEARNING MODELS

S. Haseena, Vasantha Kumar Venugopal
1 citations
International Journal of Engineering Research and Science & Technology
Energy, Environment, and Transportation Policies
0.36
article

ACCURATE PRICE PREDICTION OF USED CARS USING MACHINE LEARNING MODELS

S. Haseena, Vasantha Kumar Venugopal
article en
1 citations

Abstract

A car price prediction has been a highinterest research area, as it requires noticeable effort and knowledge of the field expert. Considerable number of distinct attributes are examined for the reliable and accurate prediction. To build a model for predicting the price of used cars. We applied machine learning (linear regression , logistic regression, Support Vector Machine and Random Forest). However, the mentioned techniques were applied to work as an ensemble and regressions. The data used for the prediction was collected Respective performances of different algorithms were then compared to find one that best suits the available data set. With the help of algorithms we have to predict the best percentage and also reveal the output with respective algorithms.

International Journal of Engineering Research and Science & TechnologyVol. 21(2)
Openalex Percentile: Top 44%
Energy, Environment, and Transportation Policies
0.36
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