Abstract:The main objective of this project is to predict the Prices of Used Cars, compare the prices and also estimate the life span of a particular car, keeping in mind various statistics of that car. It is said that a new car loses its value by 10% the moment the car is taken out from the showroom. We can easily say that the main predictor of prices in this scenario is the number is kilometers the car has been driven. secondly, we need also need to keep in mind the brands of a car, each car company have their own way of pricing their car and so the prices differ from one car to another. So, the main motive of this project is to assure that the money they would invest in the car will be worthy. For the prediction of the price of used cars we applied the supervised machine learning techniques. The predictions are based on dataset collected from various website and Kaggle Website mostly. Different techniques like multiple linear regression analysis, decision trees and k-nearest neighbors have been used to make the predictions. The predictions are then rate and compared Data Which we are collected in order to find those which provide the best performances. From this we can see that this easy problem turned out to be indeed very difficult to resolve with high accuracy. All these four methods provided performance and comparable. In the Upcoming life span, we intend to use more sophisticated algorithms to make the predictions.

| DOI: 10.17148/IARJSET.2021.81249