Examination of Machine Learning for the Prediction of Used Car Values

Abstract

At the outset, the manufacturer sets the price for vehicles of a certain brand, model, and year that come with a certain set of characteristics. Their value changes with time and the secondary market based on factors like supply and demand for their unique characteristics and their unique history. The more unique they are in comparison to other vehicles of a similar kind, the more difficult it is to evaluate them using standard techniques. A more precise valuation of a vehicle is possible with the use of Machine Learning (ML) algorithms that make greater use of data on all of its less common features. Machine learning (ML) techniques including Linear Regression, Ridge Regression, Lasso Regression, and Random Forest Regression are evaluated in this study for their ability to predict used car values. An successful tool for predicting prices relies heavily on its dataset, which improves its capacity to provide accurate forecasts in the complex and ever-changing used automobile market. Machine learning—more especially, a linear regression model trained on the carefully selected dataset—is the backbone of the Car Price Predictor. In this scenario, the characteristics included in the automobile affect its price, and linear regression, a basic statistical analysis tool, skillfully fits a linear equation to the observed data points to enable the model to predict the numeric value of the target variable. to account for depreciation, which enables a better use of past data for forecasting present pricing. The research study analyzed a large public dataset that included pre-owned automobiles.