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- W4382279863 abstract "The E-Commerce industry has taken the world in its stride. It has been growing at an exponential rate. This paper presents a machine learning-based model to predict the price of a used vehicle for selling and buying purposes. The model proposes two algorithms: Linear Regression and Random Forest Regression and draws a comparison between the two on the basis of standard performance measures. The uniqueness of this work is that it is not only capable to predict the price of a used vehicle but the model can also be extended with minimal effort for any kind of product across various spheres of the E-commerce industry." @default.
- W4382279863 created "2023-06-28" @default.
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- W4382279863 date "2023-01-01" @default.
- W4382279863 modified "2023-09-25" @default.
- W4382279863 title "Integrated Linear Regression and Random Forest Framework for E-Commerce Price Prediction of Pre-owned Vehicle" @default.
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- W4382279863 doi "https://doi.org/10.1007/978-981-99-1916-1_8" @default.
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