Comparative Analysis of Supervised Learning Algorithms for Valuating Used Car Prices

Abstract

The Indian automobile industry had its highest-ever annual domestic passenger vehicle sales last year. A total of 3.793 million or 37.93 lakh units were sold in the country in 2022, which is 23.1 percent higher than the preceding year. Similarly, the used car sale is also be increased day by day. The actual and reasonable rates of used cars are important to sale and purchase so that, buyers and sellers will be get benefited. The disparity in prices due to various characteristics or features consistently making prediction of price a difficult job. In the matter of used car price prediction, it has been equally tough. It is challenging to determine when the advertised price is indeed legitimate. Used car prices are highly affected by features like new car price, engine power (cc), maximum power (bhp). For the sake of predicting used car prices on ground of its characteristics, this research target to generate machine learning models incorporating multiple linear regression, decision tree regression and random forest regression. The Car Dekho data set that is used, initially had 13 attributes and 19974 records. In this Research Field, significant study has been carried out; although, not all of them utilized Scikit-learn. Our Suggested models produced accuracy of 94.10% with random forest regression whereas 92.45% with decision tree regression and 89.85% with multiple linear regression.