Predicting Used Car Prices Using Machine Learning: A Comparative Analysis of Regression and Ensemble Models

Abstract

The globe is expanding daily, and with it are everyone's expectations. Purchasing an automobile is one of the demands out of all of them. However, not everyone can afford a new car, so they will purchase a used one. However, newcomers are often unaware of the market value of their ideal vehicle for an old car. That's why we require a platform that assists new users in estimating car prices. We propose that platform in this work, which is built with machine learning technology. Let's attempt to create a statistical model that can forecast the cost of a used car using supervised machine learning techniques including linear regression, KNN, Random Forest, XG boost, and decision trees. We will be assisted in this endeavor by prior customer data and a certain set of characteristics. In order to choose the best model, we will also compare the forecast accuracy of different models.For buyers, this system helps assess whether the asking price of a car is fair based on market trends. Sellers can use the predictions to set competitive prices for their vehicles, ensuring better market positioning. This predictive capability ultimately enhances transparency, allowing for more informed and confident decision-making in the automotive industry. With continuous advancements in machine learning, the accuracy and efficiency of car price predictions will continue to improve, offering even greater market insights.