The variable importance score in the context of the random forest algorithm indicates the importance of each feature (variable) in predicting the target variable, which, in this case, is the price of the car. In other words, a variable is considered important if removing it from the model results in worse predictions. The importance of the variable is calculated for all trees in the forest and then the average is taken 5.

Data analysis showing the average horse power value of each engine model in the dataset

Machine learning model that attempts to establish a relationship between the engine model and the amount of power. The model uses a random forest tree process to assign a score to each model.

The scatterplot shows the result of a machine learning model that tries to predict the price of cars. As shown in the graph, only a minority of the values deviate from those predicted by the prediction model, showing a good frequency of hits with approximate values.