A Smart Used Car Price Prediction System.

Abstract

Determining whether the listed price of a used car is a challenging task, due to the many factors that drive a used vehicle’s price on the market. The focus of this project is creating a system that can precisely anticipate the cost of a used car dependent on its components Like(Brand Name, Model year, KM driven, Engine Type, Engine capacity, Transmission and color). In fact, seller also has no idea about the car’s existing value or the price he should be selling the car. To overcome this problem, we have developed a model which will be highly effective. Regression Algorithms are used because they provide us with continuous value as an output and not a categorized value. 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. Various strategies like Linear Regression, KNN, Random Forest and Decision trees have been utilized to make the prediction. We have considered various evaluation measures i.e. mean square error and R-square Usually, we are interested in accuracy in case of classification, when we deal with regression we use mean square error , R-square.