Car Price Prediction:An Application of Machine Learning

Abstract

In order to determine the worthiness of a car based on a variety of factors using machine learning models. In this study, the challenge is to prevent the model from becoming overfit and to generalize it. A combination of regularization techniques as well as hyperparameter tuning techniques was employed to overcome this challenge. Develop linear regression, lasso regression, ridge regression, elastic net regression, random forest, decision tree and Support Vector Machine models with hyperparameters. The objective of this article is to build a generalized model that can predict the price of used cars based on some factors, such as the car's mileage, the year it was made, the road tax, the type of fuel it uses, the size of its engine etc. Optimal model can help sellers, buyers, and car manufacturers. A relatively accurate prediction of price can be made based on information provided by users. Among the seven models, the support vector regressor is the optimal model based on the evaluation metrics such as R Squared (R^2) of 95.27%, Mean Absolute Error (MAE) of 0.142, Mean Squared Error (MSE) of 0.047, and Root Mean Squared Error (RMSE) of 0.218 at 90% of the train data and 10% of the validation data.