SMART PRICING SOLUTIONS FOR USED CARS USING DECISION TREE AND RANDOM FOREST MODEL

The used car market is vast and is affected by many parameters, making it is essential to predict prices of used cars. This work recommends customers in making informed decisions while planning to purchase used car. Presently, Machine Learning emerged as the most effective methods for prediction of prices of used cars considering correlated factors such as mileage and vehicle age. This work applied two machine learning models includes Decision Tree and Random Forests on large used car dataset so collected from Kaggle. The prediction accuracy and precision of these models thoroughly evaluated. The comparison of results output of two models is made to find out the effectiveness and reliability for analyzing large database. The study concludes that Random forest model emerged as the best model in comparison to Decision Tree model in prediction accuracy.