Predicting the Prices of the Used Cars using Machine Learning for Resale

In this paper, we discuss the use of machine learning models that can accurately forecast the price of a used car based on its attributes and features. In this study, we look at applying supervised machine learning models to forecast second-hand car prices in India. The forecasts are based on historical information gathered from daily news articles, magazines and from various standard websites. The predictions were made using a variety of models, including Random Forest Regressor, Extra Tree Regressor, Bagging Regressor, Decision Tree and the XG Boost method. The most accurate predictions are then selected after comparing the predictions of the models applied. The five strategies all delivered performance that was almost equivalent but not equal and the results obtained are comparable. There are many distinct properties that can be measured, and it has taken a lot of effort to forecast the outcome with increased reliability and accuracy. These models are compared based on their prediction accuracy to determine which is more accurate. Our findings demonstrate that the Random Forest model produces the greatest outcomes.