International Journal For Multidisciplinary Research

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Used Car Price Prediction Using Random Forest Algorithm

Author(s) Dipti A. Gaikwad, Pratik S. Suwarnakar, Yash R. Mahajan, Amita U. Petkar, Shreyasi G. Theurkar
Country India
Abstract The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user’s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user’s inputs. To evaluate the performance of each regression, R-square was calculated.
Keywords Used car price prediction, Regression, Linear Regression, Lasso Regression, SVM, Random Forest, and Machine Learning.
Field Engineering
Published In Volume 5, Issue 3, May-June 2023
Published On 2023-05-31
Cite This Used Car Price Prediction Using Random Forest Algorithm - Dipti A. Gaikwad, Pratik S. Suwarnakar, Yash R. Mahajan, Amita U. Petkar, Shreyasi G. Theurkar - IJFMR Volume 5, Issue 3, May-June 2023. DOI 10.36948/ijfmr.2023.v05i03.3308
DOI https://doi.org/10.36948/ijfmr.2023.v05i03.3308
Short DOI https://doi.org/gr97th
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