Pre-owned car price prediction by employing machine learning techniques
DOI:
https://doi.org/10.31181/jdaic10008102023nKeywords:
Price Prediction, Machine Learning, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE)Abstract
Pre-owned automobiles including cars are becoming incredibly popular. There has been a steady increase in automobile production namely, passenger cars over the preceding decade with more than 70 million passenger cars being manufactured in 2016 itself. This has given rise to the resale automobile market, which has become a thriving business in its own right. Customers who are interested in purchasing a pre-owned car frequently face the difficulty in locating a vehicle that fits within their financial constraints as well as estimating the price of a specific pre-owned car. Customers can make more educated decisions regarding the purchase of a pre-owned car if they have access to accurate price projections for pre-owned cars. With the proliferation of digital marketplaces, both the buyer and the seller remain more updated regarding the recent market trends and patterns that impact the value of a used car. In this paper, we investigate this issue and propose a forecasting system using machine learning techniques that enables a prospective buyer to anticipate the price of a pre-owned vehicle of interest. The process is conducted with the collection and pre-processing of a dataset followed by an exploratory data analysis. Various machine learning regression techniques, such as Linear Regression, LASSO (Least Absolute Shrinkage and Selection Operator) Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting, have subsequently been implemented. The techniques are then compared so as to determine an optimal solution. Three types of errors namely, MAE, MSE and RMSE have also been calculated in order to determine the best-fitted model.
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References
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