A Deep Learning Approach for Used Car Price Prediction
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Deep Learning, Used Car Price PredictionAbstract
Buying a used car can be a challenging experience. Like many other consumer goods, used car prices have risen rapidly in recent years. In addition, gasoline prices and rising interest rates have made the experience of owning a car even more painful. In this research, we propose an intelligent framework for estimating the cost of used cars using artificial neural network algorithms. The model was developed using a training dataset of 140,000 used vehicles from 30 popular US car brands. The model's predictions are validated against a test data set of 35,000 used cars. Numerous features are examined for reliable and accurate predictions. Artificial neural networks are built using the Keras regression algorithm, and their performance is compared to basic models such as linear regression, decision tree algorithms, gradient boosting, and random forests. Categorical variables were processed using embedding techniques to improve predictive performance. The results are consistent with actual values and significantly improved over the baseline model. Experimental results showed that an ANN model with a mean absolute percentage error of 11percent and an R2 value of 0.96 outperforms the random forest model with a MAPE of 14 percent and an R2 value of 0.94.
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Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
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Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.