A Random Forest-based Classification Method for Prediction of Car Price

Data analytics is playing an important role in e-commerce, in which the classification algorithms like linear regression and Random forest help in buying and selling the product as per market demand. Customer needs identification is a very challenging task. In this proposed work, we have developed an algorithm to predict the price of a car based on the various attributes of the car. In this work, two algorithms: linear regression and the random forest were implemented which can predict the correct market value of a car model for the considered data set and the results are compared using various statistical parameters.