Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{178525,
        author = {A. Rakul and A. Kabilesh and J. Mithun Raj and V. Santhosh and T. Arachelvi},
        title = {ELECTRIC VEHICLE PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5217-5222},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178525},
        abstract = {This Streamlit-based application uses a trained Random Forest regression model to forecast the volume of electric vehicle sales worldwide. A CSV file with test data can be uploaded by users, and the application will process it by eliminating missing data, cleaning out unnecessary columns, and converting values to numeric representation. A pre-trained scaler is then utilized to scale the cleaned data to the format specified during model training. The application uses metrics like Mean Squared Error and R-squared score to assess model performance and makes predictions based on the processed data. Interactive outputs, such as data tables, prediction results, and various visualizations including feature importance, actual versus predicted values, residual plots, and distribution of prediction errors, are displayed using Streamlit. When combined, these elements offer a user-friendly interface for examining the efficacy and behavior of the model.},
        keywords = {Electric car sales prediction, random forest regression, data visualization, streamlit application},
        month = {May},
        }