ARCHIVES

Abstract: The manufacturer sets the price of a new car on the market, along with certain extra expenses paid by the government in the form of taxes. Customers may be confident that their new car purchase will be worth their money. Yet, sales of used automobiles are rising globally as a result of new car price increases and the financial instability of the consumers who purchase them. As a result, a Used Vehicle Price Prediction system with a recommendation system is required. This system efficiently assesses the value of the used car utilizing a number of factors. The current system involves a procedure where a vendor chooses a price at random and the buyer is unaware of the car and its current market value. In actuality, neither the seller nor the price at which he should sell the automobile has any notion of its current value. Also, the seller's prediction of the car's cost may not be more accurate, which could result in a loss. I proposed a model that will be very accurate to solve this issue. Regression algorithms like linear, lasso, and random forest are employed since their output is a continuous value rather than a categorized one. As a result, it will be possible to estimate a car's exact price rather than just its price range. A user interface that accepts input from any user and displays the car pricing in accordance with user inputs has been installed.