Used Car Price Prediction Using Machine Learning
Prof. Dipti Sawant
, Pratik Suwarnakar , Yash Mahajan , Amita Petkar , Shreyasi Theurkar
Used car price prediction, Regression, Linear Regression, Lasso Regression, Random Forest, and Machine Learning.
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.
"Used Car Price Prediction Using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 3, page no.553 - 556, March-2023, Available :https://ijsdr.org/papers/IJSDR2303087.pdf
Volume 8
Issue 3,
March-2023
Pages : 553 - 556
Paper Reg. ID: IJSDR_204389
Published Paper Id: IJSDR2303087
Downloads: 000347330
Research Area: Engineering
Country: Pune, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
