Price prediction of second-hand cars using Regression Models: A comprehensive study

Economic changes and recessions put pressure on citizens, and the buyers' inclination to use second-hand commodities increases as a result. Cars are one of these goods, and trading second-hand cars is booming especially in developing countries, which is where buyers cannot afford a new car or people desire to make income by selling their assets. Determining the price may be challenging, so both buyers and sellers welcome a framework in regards to determining the price or identifying the influential features in order to determine the price. Several machine learning approaches, such as linear regression, ridge regression, lasso regression, random forest, bagging, and support vector regression are used in this study in order to provide a framework in regards to predicting the pricing of used cars. These techniques are regression-based, and their goal is to identify the regression line with the lowest absolute error value. Finally, the approaches that are used in order to provide buyers, sellers, and other participants in the second-hand automobile market with a solid statistical framework were carefully analyzed.