Car-Economics: Forecasting Prices in the Pre-Owned Market Using Machine Learning
Keywords:Machine Learning, Decision Tree, Random Forest, Database, Python
The purchase price of a new car and a few additional costs are determined by the company that makes the vehicle. Used vehicle sales are increasing globally as a result of rising new car prices and people’s unwillingness to afford them. This strategy is successful since the vendor often sets the price impulsively and the buyer normally isn’t aware of all the features and the car’s market value. According to research, figuring out a used car’s fair market value is both difficult and important. Consequently, it is necessary to create a precise method for estimating used automobile cost. In this case, machine learning prediction approaches could be helpful. The model has to be trained on the massive dataset using techniques like random forest and decision tree before it can be used. Our primary objective is to develop a model that, given the information provided by the client, can accurately and dependably anticipate the selling price of a used car. In this project, our team designed a stunning User Interface (UI) that asks customers for comments and provides pricing estimates. The database stores both the user inputs and the predicted cost of the automobile.
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