USED CAR

Using a decision tree algorithm, I examined a group of used automobiles and forecasted their asking prices based on brand, model, age, mileage, fuel type, and transmission type. To improve the model’s performance, I determined the best value for the minimum split, determined the nodes with the highest and lowest probabilities, and described how they relate to the dataset’s attributes. To demonstrate the decision tree’s value for pricing analysis, I also used it to predict the outcomes of a few sample data points. I completed all the work in Python using a Jupyter Notebook.