A Comparative Analysis of Regression Models for used Car Price Prediction
DOI:
https://doi.org/10.15662/IJEETR.2026.0802247Keywords:
Used car price prediction, regression analysis, ensemble learning, deep learning models, explainable artificial intelligence, LIME and SHAP, Flask-based deployment, machine learning evaluation metricsAbstract
The accurate calculation of used cars prices is still a major problem in the online market and intelligent transportation because market and car characteristics are not homogeneous hence dictate price variation. Strong regression modelling can help in improving transparency, trust, and decision-making process of both consumers and vendors significantly. The paper involves comparative analysis of deep learning and many regression models, using the CarDekho data of vehicles to predict the prices of used cars. The whole data preprocessing process includes null value cleaning, duplicate trimming of data, target variable logarithmic transformation, feature cleaning, IQR used to remove outliers, and labelling encoding. Classical and modern paradigm of learning are all viable and applicable, and each of these models can be evaluated. The efficacy of the model is assessed using the MAE, MAPE, RMSE and R 2 indicators. The outcomes of the experiment show that the quality of prediction of tree-based and ensemble models is higher. The MAPE of the Voting Regressor is 13.1 and it has R 2 of 95.7 whereas the random forest model has a R 2 of 93.4. SHAP and LIME are AI techniques that can be explained and are applied to estimate the contribution of features to improve the transparency of models or explain the behaviour of prediction. Moreover, the application that is being used to practically implement the trained models is a Flask-based web application, with SQLite-supported authentication to offer real-time predictions of the uploaded data, which is provided by the users
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