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y = df.<column>
features = ['col1', 'col2', 'col3']
X = df[features]
X.head()
from sklearn.tree import DecisionTreeRegressor model = DecisionTreeRegressor(random_state = 1)
model.fit(X, y)
model.predict(<new data>)