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Created August 4, 2021 08:01
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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(), ytrain)
predictions = model.predict(xtest)
from sklearn.metrics import mean_absolute_error
model.score(xtest, predictions)
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