  Cars are being sold more than ever. Developing countries adopt the lease
culture instead of buying a new car due to affordability. Therefore, the rise
of used cars sales is exponentially increasing. Car sellers sometimes take
advantage of this scenario by listing unrealistic prices owing to the demand.
Therefore, arises a need for a model that can assign a price for a vehicle by
evaluating its features taking the prices of other cars into consideration. In
this paper, we use supervised learning method namely Random Forest to predict
the prices of used cars. The model has been chosen after careful exploratory
data analysis to determine the impact of each feature on price. A Random Forest
with 500 Decision Trees were created to train the data. From experimental
results, the training accuracy was found out to be 95.82%, and the testing
accuracy was 83.63%. The the model can predict the price of cars accurately by
choosing the most correlated features.
