car price4.py

predict = "price"
data = data[["symboling", "wheelbase", "carlength", 
             "carwidth", "carheight", "curbweight", 
             "enginesize", "boreratio", "stroke", 
             "compressionratio", "horsepower", "peakrpm", 
             "citympg", "highwaympg", "price"]]
x = np.array(data.drop([predict], 1))
y = np.array(data[predict])

from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2)

from sklearn.tree import DecisionTreeRegressor
model = DecisionTreeRegressor()
model.fit(xtrain, ytrain)
predictions = model.predict(xtest)

from sklearn.metrics import mean_absolute_error
model.score(xtest, predictions)